diff --git a/README.md b/README.md
index 6499bfc64dd049ac0b5d0d2f19cc66f65cae4218..75454b82f6ff88213243f96358fa4e9b358ce3ef 100644
--- a/README.md
+++ b/README.md
@@ -17,6 +17,12 @@ This repository contains the frontend code to display TabArena leaderboard.
 The leaderboard is hosted on a HuggingFace space.
 
 Reference:
-* Website: tabarena.ai
+* Website: https://tabarena.ai
 * Paper: TBA
 * TabArena Codebase: https://tabarena.ai/code
+
+# Install LB Code for Development
+
+```bash
+pip install -e ".[dev]"
+```
\ No newline at end of file
diff --git a/data/full-cls/figures/critical-diagram.pdf b/data/full-cls/figures/critical-diagram.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..1daefa080392772c3b1a2ed5670c4da458f152fc
Binary files /dev/null and b/data/full-cls/figures/critical-diagram.pdf differ
diff --git a/data/full-cls/figures/critical-diagram.png.zip b/data/full-cls/figures/critical-diagram.png.zip
new file mode 100644
index 0000000000000000000000000000000000000000..944b931e98d50e422b34fb2f61567f648bdbbe71
--- /dev/null
+++ b/data/full-cls/figures/critical-diagram.png.zip
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:308a9d416aaaff64659e2ed5f56ed2cf782ca266a5ef0ded86ed835659327f4d
+size 295101
diff --git a/data/full-cls/leaderboard.tex b/data/full-cls/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..e3dd1a6a7d505b882f934ad3a4150b2ca8842a80
--- /dev/null
+++ b/data/full-cls/leaderboard.tex
@@ -0,0 +1,49 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+TabM (T+E) & \textcolor{gold}{\textbf{1612${}_{-32,+43}$}} & \textcolor{silver}{\textbf{0.492}} & \textcolor{gold}{\textbf{7.8}} & \textcolor{silver}{\textbf{3.0}} & \textcolor{silver}{\textbf{6}} & \textcolor{silver}{\textbf{5.7\%}} & 2466.21 & 1.50 \\
+AutoGluon 1.3 (4h) & \textcolor{silver}{\textbf{1611${}_{-34,+36}$}} & \textcolor{gold}{\textbf{0.564}} & \textcolor{gold}{\textbf{7.8}} & \textcolor{gold}{\textbf{2.4}} & \textcolor{gold}{\textbf{11}} & \textcolor{gold}{\textbf{4.6\%}} & 1322.72 & 2.36 \\
+LightGBM (T+E) & \textcolor{bronze}{\textbf{1570${}_{-31,+37}$}} & 0.404 & \textcolor{bronze}{\textbf{9.1}} & 5.4 & 1 & 7.7\% & 382.05 & 1.49 \\
+RealMLP (T+E) & \textcolor{bronze}{\textbf{1570${}_{-32,+41}$}} & \textcolor{bronze}{\textbf{0.419}} & 9.2 & \textcolor{bronze}{\textbf{5.2}} & 1 & 7.1\% & 5534.77 & 3.87 \\
+TabM (T) & 1515${}_{-24,+40}$ & 0.407 & 11.0 & \textcolor{bronze}{\textbf{5.2}} & 1 & 6.8\% & 2466.21 & 0.18 \\
+CatBoost (T+E) & 1503${}_{-25,+33}$ & 0.379 & 11.5 & 6.9 & 1 & \textcolor{bronze}{\textbf{6.7\%}} & 1372.94 & 0.56 \\
+CatBoost (T) & 1498${}_{-35,+36}$ & 0.365 & 11.8 & 6.1 & 1 & 6.9\% & 1372.94 & 0.07 \\
+LightGBM (T) & 1486${}_{-27,+41}$ & 0.318 & 12.1 & 10.2 & 0 & 8.5\% & 382.05 & 0.25 \\
+XGBoost (T+E) & 1478${}_{-31,+37}$ & 0.321 & 12.5 & 8.0 & 0 & 8.5\% & 685.87 & 1.45 \\
+CatBoost (D) & 1475${}_{-33,+42}$ & 0.345 & 12.6 & 5.9 & 2 & 8.2\% & 5.72 & 0.08 \\
+ModernNCA (T) & 1434${}_{-25,+45}$ & 0.278 & 14.2 & 6.8 & 2 & 8.3\% & 4879.89 & 0.52 \\
+ModernNCA (T+E) & 1433${}_{-39,+32}$ & 0.373 & 14.4 & \textcolor{bronze}{\textbf{5.2}} & 2 & 8.1\% & 4879.89 & 8.74 \\
+XGBoost (T) & 1431${}_{-29,+44}$ & 0.266 & 14.3 & 11.6 & 0 & 9.0\% & 685.87 & 0.21 \\
+TabM (D) & 1395${}_{-30,+35}$ & 0.271 & 16.0 & 9.9 & 0 & 10.6\% & 10.21 & 0.14 \\
+TorchMLP (T+E) & 1377${}_{-34,+33}$ & 0.222 & 16.7 & 11.1 & 0 & 9.6\% & 2389.22 & 2.16 \\
+RealMLP (T) & 1373${}_{-30,+41}$ & 0.202 & 16.8 & 12.3 & 0 & 10.3\% & 5534.77 & 0.19 \\
+EBM (T+E) & 1372${}_{-25,+31}$ & 0.180 & 16.8 & 10.3 & 0 & 12.7\% & 914.23 & 0.22 \\
+FastaiMLP (T+E) & 1332${}_{-33,+35}$ & 0.196 & 18.6 & 10.6 & 0 & 13.1\% & 618.90 & 4.77 \\
+ModernNCA (D) & 1323${}_{-34,+33}$ & 0.134 & 19.1 & 10.2 & 1 & 12.6\% & 14.78 & 0.35 \\
+EBM (T) & 1306${}_{-29,+36}$ & 0.122 & 19.7 & 14.4 & 0 & 13.5\% & 914.23 & 0.03 \\
+EBM (D) & 1274${}_{-31,+39}$ & 0.136 & 21.1 & 7.8 & 3 & 14.6\% & 4.31 & 0.05 \\
+XGBoost (D) & 1264${}_{-29,+31}$ & 0.105 & 21.6 & 16.6 & 0 & 12.1\% & 1.77 & 0.12 \\
+ExtraTrees (T+E) & 1259${}_{-26,+38}$ & 0.104 & 21.8 & 13.1 & 0 & 14.1\% & 189.76 & 0.74 \\
+TorchMLP (T) & 1250${}_{-33,+36}$ & 0.096 & 22.2 & 18.0 & 0 & 12.3\% & 2389.22 & 0.15 \\
+TabDPT (D) & 1244${}_{-31,+39}$ & 0.176 & 22.4 & 5.7 & \textcolor{bronze}{\textbf{4}} & 13.6\% & 22.61 & 8.55 \\
+RealMLP (D) & 1243${}_{-30,+39}$ & 0.090 & 22.5 & 18.5 & 0 & 12.6\% & 35.38 & 0.20 \\
+FastaiMLP (T) & 1233${}_{-35,+32}$ & 0.089 & 23.0 & 17.9 & 0 & 15.2\% & 618.90 & 0.30 \\
+RandomForest (T+E) & 1221${}_{-37,+35}$ & 0.107 & 23.5 & 12.6 & 0 & 14.6\% & 323.74 & 0.74 \\
+ExtraTrees (T) & 1207${}_{-32,+37}$ & 0.079 & 24.1 & 14.9 & 0 & 15.5\% & 189.76 & 0.08 \\
+LightGBM (D) & 1206${}_{-31,+34}$ & 0.076 & 24.0 & 21.1 & 0 & 13.5\% & 1.79 & 0.12 \\
+RandomForest (T) & 1166${}_{-33,+35}$ & 0.076 & 25.8 & 15.1 & 0 & 16.0\% & 323.74 & 0.08 \\
+TorchMLP (D) & 1080${}_{-37,+35}$ & 0.021 & 28.9 & 26.2 & 0 & 17.8\% & 6.83 & 0.15 \\
+FastaiMLP (D) & 1052${}_{-33,+32}$ & 0.026 & 29.9 & 26.8 & 0 & 20.8\% & 2.91 & 0.37 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.011 & 31.4 & 29.3 & 0 & 22.6\% & 0.38 & 0.04 \\
+Linear (T+E) & 996${}_{-32,+37}$ & 0.042 & 31.6 & 22.6 & 0 & 27.7\% & 51.79 & 0.22 \\
+Linear (T) & 958${}_{-32,+36}$ & 0.026 & 32.6 & 24.7 & 0 & 28.7\% & 51.79 & 0.08 \\
+Linear (D) & 950${}_{-35,+34}$ & 0.019 & 32.8 & 17.7 & 1 & 29.9\% & 1.61 & 0.10 \\
+ExtraTrees (D) & 926${}_{-40,+43}$ & 0.010 & 33.4 & 30.0 & 0 & 25.4\% & 0.25 & 0.04 \\
+KNN (T+E) & 695${}_{-49,+48}$ & 0.000 & 37.7 & 37.4 & 0 & 47.7\% & 3.57 & 0.19 \\
+KNN (T) & 606${}_{-51,+45}$ & 0.000 & 38.7 & 38.6 & 0 & 49.6\% & 3.57 & 0.04 \\
+KNN (D) & 451${}_{-61,+67}$ & 0.000 & 40.0 & 39.9 & 0 & 58.1\% & 0.07 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/full-cls/tabarena_leaderboard.csv b/data/full-cls/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c67221bbadf4981061f87049214644c56600510d
--- /dev/null
+++ b/data/full-cls/tabarena_leaderboard.csv
@@ -0,0 +1,42 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+TABM (tuned + ensemble),34341.376551290836,9.60910518720136,4223.58680397795,3.4317121842487635,0.5077918677314276,0.4683217107084951,0.05713135019989292,0.0464892851251854,45864.929770826835,134.55326674312073,7.828947368421052,0.17047,8245.070318195554,2.6588550435172187,2466.2108648716858,1.5022465694891438,0.47136645123500964,0.4458821771498494,0.018407448383057634,0.00923027101272119,39661.48674821285,121.43882105497326,5.5,6,7,2,23,1612.0,43.0,31.6,0.8292763157894737,0.3331708131050236
+AutoGluon 1.3 (4h),7918.9967654708535,21.9962719316371,2834.6060407147975,3.0873029091200266,0.43572604440948826,0.37369319622384795,0.04552082156008848,0.02756565625162793,31011.170971980166,263.893057970457,7.842105263157895,0.159155,7086.632828738954,3.4718479580349393,1322.7174946761893,2.35500290690449,0.3593825434326649,0.3179495368931894,0.020277285974108883,0.008552420217732355,23372.022224152715,142.27612475951975,4.0,11,1,4,22,1611.2,35.5,33.4,0.8289473684210527,0.40970136828052783
+GBM (tuned + ensemble),2957.8209862183407,11.98416593290909,759.1477152793035,2.5417865978850473,0.5957286671834613,0.5543276443877001,0.0768692051258278,0.04754330288453222,8568.794143617388,184.93302208876983,9.131578947368421,0.16698000000000002,1552.8742877377404,3.4757837878333198,382.05361557599804,1.4876036641335277,0.6251845339504845,0.567482310945806,0.035330419910613864,0.01740708281579771,7514.342598912193,103.35126359750211,7.75,1,3,1,33,1570.0,36.6,30.1,0.7967105263157894,0.18453518660562004
+REALMLP (tuned + ensemble),75228.3606640392,17.6439790335315,5880.304462786179,8.382833590222491,0.5811880944579212,0.5195249746896198,0.0707111257986013,0.04178949688851757,134045.19581697704,292.40009500207566,9.157894736842104,0.166985,21596.535681260957,5.895895957946777,5534.7676914745925,3.874032586779933,0.5453504649932683,0.5412877121937096,0.030830693333615045,0.017527041551138753,90524.58108841689,250.02806627683862,8.0,1,3,4,30,1569.6,40.5,31.7,0.7960526315789473,0.1918840950297688
+TABM (tuned),34341.376551290836,1.0683778852050068,4223.58680397795,0.37888409513825755,0.5928893193157706,0.5290373167203445,0.0681053050300204,0.060722902369039045,45864.929770826835,13.890136033418576,11.039473684210526,0.172585,8245.070318195554,0.2723346948623657,2466.2108648716858,0.17557376557934842,0.5877464635288101,0.5713169031682397,0.03216876796042978,0.018156210036345113,39661.48674821285,10.546435553124088,11.0,1,5,3,29,1515.2,40.0,23.2,0.7490131578947369,0.1928119823795554
+CAT (tuned + ensemble),16504.07579820414,2.581779239609925,3201.0265707590415,0.8915752484646539,0.6209138650557025,0.5659344601903572,0.06713271363366402,0.0405214229704263,27193.868813721343,44.08651163500922,11.486842105263158,0.16040500000000002,5046.900271190538,1.2630292971928916,1372.9411122807264,0.5562989902961428,0.6153632608570143,0.6088415859515164,0.03472659799727995,0.022138825584558906,20541.46486167592,37.76547447057217,10.0,1,1,1,35,1502.8,32.3,24.1,0.7378289473684211,0.14412801379906642
+CAT (tuned),16504.07579820414,0.42638187025025576,3201.0265707590415,0.12024042770987867,0.6346801229162512,0.5780524851760976,0.06937153661241705,0.04017281020087998,27193.868813721343,6.229177098348047,11.75,0.161705,5046.900271190538,0.12877851062350804,1372.9411122807264,0.07385371803542701,0.6314852452199684,0.6377829597443649,0.036574094595854534,0.02550717929301198,20541.46486167592,4.9725379405984995,12.0,1,3,2,32,1498.2,35.5,34.4,0.73125,0.16319274327461483
+GBM (tuned),2957.8209862183407,1.875632255676894,759.1477152793035,0.5049369881097493,0.6821201235665096,0.6172522665748253,0.08458189231987852,0.057321874164711774,8568.794143617388,32.37091249654915,12.092105263157896,0.16877999999999999,1552.8742877377404,0.5045170254177518,382.05361557599804,0.2538713244201605,0.7168421399910502,0.6480642307818962,0.039126655178203906,0.024837453552070406,7514.342598912193,15.175591971121808,11.0,0,0,0,38,1486.3,40.1,26.7,0.7226973684210526,0.097905840634771
+XGB (tuned + ensemble),5957.374200563333,6.456417350253166,1167.526219278486,2.7907218103162132,0.6794057655741552,0.6314178768057309,0.08548974406302369,0.05849041431964196,10832.944520166355,137.58836217438702,12.513157894736842,0.165745,1680.0658507664998,2.2809726662105985,685.86510540535,1.4547593315263065,0.7126419091535116,0.6462162901594695,0.05102562935485577,0.027448154499449852,8251.297786111467,74.47019952683146,10.5,0,2,0,36,1478.0,36.4,30.3,0.712171052631579,0.12563292181614538
+CAT (default),220.2627424415789,0.2710267032099049,109.9732905896213,0.13662992734425208,0.6551829671744798,0.6178972125596192,0.08194323858116616,0.044223095820168916,404.9759898295662,6.476751807794606,12.552631578947368,0.16373,18.264583627382912,0.17750852637820774,5.723546572951673,0.0761539571798428,0.6872314742824219,0.6578183507970544,0.04295775865131041,0.020841879336463684,103.69107151573829,5.120402547941957,14.0,2,2,2,32,1475.0,41.3,32.8,0.7111842105263158,0.1691212806814804
+MNCA (tuned),57356.284085895306,20.170870465214488,5990.817791505437,2.0485901061394993,0.721681429841744,0.6240148041756003,0.08261656685639085,0.061724748753513047,80456.91211763977,129.4389431731015,14.197368421052632,0.17054999999999998,14186.536935488384,0.6020842525694106,4879.890404506269,0.5247194359730172,0.7744612480983963,0.6453947144753973,0.05917481703324834,0.03283118689868241,66956.02864547497,27.735396922747526,13.0,2,1,1,34,1434.4,44.3,24.2,0.6700657894736842,0.14750425482010807
+XGB (tuned),5957.374200563333,1.301870340352867,1167.526219278486,0.6678561539347552,0.7344435643624297,0.6738277145612771,0.0896002321196869,0.06302079730298164,10832.944520166355,28.47499330573137,14.289473684210526,0.16848000000000002,1680.0658507664998,0.3827125522825453,685.86510540535,0.2050912539994952,0.7444722954009122,0.6783301736911117,0.05914830069662402,0.031553385715146064,8251.297786111467,11.461364611937853,13.0,0,0,0,38,1430.7,43.2,29.0,0.6677631578947368,0.08653175576997406
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+TABM (default),150.0762373017289,1.2507152029645372,19.886819500646006,0.46487430096802723,0.7289050360420961,0.6965301783060158,0.10592279294349498,0.08101332667275364,189.61145131944258,14.042337078320946,15.986842105263158,0.17246,31.126562476158142,0.20260944763819377,10.213381764059356,0.1381032773929915,0.8404844924976476,0.7209220250926951,0.04116190641707207,0.026799112086523788,144.96049255349743,10.968725907290855,15.5,0,1,0,37,1394.6,34.3,29.3,0.625328947368421,0.1012312665218443
+NN_TORCH (tuned + ensemble),24331.947126566527,16.31368946276213,3050.9102763481856,4.325404104362444,0.77831117981961,0.7280978052549151,0.09611794604638334,0.06918787987081731,56038.718047349466,227.24142067178357,16.657894736842106,0.17071999999999998,9097.789536105262,5.056680162747702,2389.2199648500327,2.157502904371376,0.9031626632034824,0.7953860237848694,0.057815073109235726,0.04001808164772668,44480.90343297912,173.17543399472862,16.0,0,1,0,37,1377.4,32.9,33.9,0.6085526315789473,0.08992242121508189
+REALMLP (tuned),75228.3606640392,0.8387730677922566,5880.304462786179,0.4572085739395715,0.7977234575542793,0.7117970907679741,0.10277317429931199,0.07240302920804677,134045.19581697704,13.981658721822997,16.763157894736842,0.16874,21596.535681260957,0.2674341731601291,5534.7676914745925,0.19066645138299287,0.8787741747777617,0.7098417849798737,0.06513447344574208,0.030556202222580157,90524.58108841689,11.215626382435188,15.0,0,0,1,37,1372.9,40.7,29.8,0.6059210526315789,0.08155448908452341
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+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+AutoGluon 1.3 (4h) & \textcolor{gold}{\textbf{1570${}_{-31,+30}$}} & \textcolor{gold}{\textbf{0.564}} & \textcolor{gold}{\textbf{9.7}} & \textcolor{silver}{\textbf{3.3}} & \textcolor{silver}{\textbf{6}} & \textcolor{gold}{\textbf{6.9\%}} & 1322.72 & 2.36 \\
+TabM (T+E) & \textcolor{gold}{\textbf{1570${}_{-29,+28}$}} & \textcolor{bronze}{\textbf{0.492}} & \textcolor{gold}{\textbf{9.7}} & 4.5 & 3 & \textcolor{bronze}{\textbf{8.2\%}} & 2466.21 & 1.50 \\
+LightGBM (T+E) & \textcolor{bronze}{\textbf{1534${}_{-30,+29}$}} & 0.404 & \textcolor{bronze}{\textbf{11.1}} & 6.3 & 1 & 9.8\% & 382.05 & 1.49 \\
+RealMLP (T+E) & 1531${}_{-30,+31}$ & 0.419 & 11.2 & 7.7 & 0 & 9.2\% & 5534.77 & 3.87 \\
+TabICL (D) & 1525${}_{-31,+22}$ & \textcolor{silver}{\textbf{0.501}} & 11.4 & \textcolor{bronze}{\textbf{3.5}} & \textcolor{silver}{\textbf{6}} & \textcolor{silver}{\textbf{7.9\%}} & 8.68 & 1.74 \\
+TabM (T) & 1484${}_{-30,+28}$ & 0.407 & 13.2 & 6.4 & 1 & 9.3\% & 2466.21 & 0.18 \\
+CatBoost (T+E) & 1476${}_{-26,+24}$ & 0.379 & 13.6 & 8.8 & 0 & 9.1\% & 1372.94 & 0.56 \\
+CatBoost (T) & 1468${}_{-29,+23}$ & 0.365 & 13.8 & 7.0 & 1 & 9.3\% & 1372.94 & 0.07 \\
+LightGBM (T) & 1460${}_{-28,+29}$ & 0.318 & 14.3 & 12.0 & 0 & 10.6\% & 382.05 & 0.25 \\
+XGBoost (T+E) & 1451${}_{-28,+28}$ & 0.321 & 14.7 & 9.3 & 0 & 10.7\% & 685.87 & 1.45 \\
+CatBoost (D) & 1450${}_{-27,+25}$ & 0.345 & 14.7 & 7.1 & 1 & 10.3\% & 5.72 & 0.08 \\
+TabPFNv2 (T+E) & 1450${}_{-32,+26}$ & 0.486 & 14.7 & \textcolor{gold}{\textbf{3.1}} & \textcolor{gold}{\textbf{8}} & 9.7\% & 3008.22 & 20.85 \\
+ModernNCA (T) & 1412${}_{-31,+25}$ & 0.278 & 16.5 & 9.7 & 1 & 10.5\% & 4879.89 & 0.52 \\
+ModernNCA (T+E) & 1410${}_{-30,+26}$ & 0.373 & 16.6 & 7.5 & 0 & 10.4\% & 4879.89 & 8.74 \\
+XGBoost (T) & 1408${}_{-26,+30}$ & 0.266 & 16.6 & 13.2 & 0 & 11.1\% & 685.87 & 0.21 \\
+TabPFNv2 (T) & 1384${}_{-29,+34}$ & 0.379 & 17.8 & 5.1 & 1 & 12.1\% & 3008.22 & 0.51 \\
+TabM (D) & 1373${}_{-27,+30}$ & 0.271 & 18.3 & 12.1 & 0 & 12.6\% & 10.21 & 0.14 \\
+TabPFNv2 (D) & 1363${}_{-32,+27}$ & 0.348 & 18.8 & 4.7 & 4 & 13.0\% & 3.37 & 0.32 \\
+TorchMLP (T+E) & 1358${}_{-26,+29}$ & 0.222 & 19.2 & 14.9 & 0 & 11.6\% & 2389.22 & 2.16 \\
+RealMLP (T) & 1354${}_{-27,+31}$ & 0.202 & 19.3 & 15.2 & 0 & 12.3\% & 5534.77 & 0.19 \\
+EBM (T+E) & 1352${}_{-28,+27}$ & 0.180 & 19.4 & 13.5 & 0 & 14.9\% & 914.23 & 0.22 \\
+FastaiMLP (T+E) & 1318${}_{-28,+24}$ & 0.196 & 21.1 & 12.7 & 0 & 14.6\% & 618.90 & 4.77 \\
+ModernNCA (D) & 1306${}_{-27,+27}$ & 0.134 & 21.7 & 12.3 & 1 & 14.7\% & 14.78 & 0.35 \\
+EBM (T) & 1293${}_{-31,+24}$ & 0.122 & 22.4 & 17.9 & 0 & 15.6\% & 914.23 & 0.03 \\
+EBM (D) & 1262${}_{-36,+27}$ & 0.136 & 23.9 & 11.4 & 1 & 16.6\% & 4.31 & 0.05 \\
+XGBoost (D) & 1255${}_{-32,+28}$ & 0.105 & 24.3 & 19.1 & 0 & 14.1\% & 1.77 & 0.12 \\
+ExtraTrees (T+E) & 1250${}_{-28,+27}$ & 0.104 & 24.5 & 16.7 & 0 & 15.8\% & 189.76 & 0.74 \\
+TorchMLP (T) & 1238${}_{-27,+28}$ & 0.096 & 25.0 & 21.5 & 0 & 14.1\% & 2389.22 & 0.15 \\
+RealMLP (D) & 1236${}_{-25,+24}$ & 0.090 & 25.2 & 21.0 & 0 & 14.6\% & 35.38 & 0.20 \\
+TabDPT (D) & 1236${}_{-35,+30}$ & 0.176 & 25.1 & 8.2 & 2 & 15.5\% & 22.61 & 8.55 \\
+FastaiMLP (T) & 1221${}_{-26,+28}$ & 0.089 & 25.9 & 21.0 & 0 & 16.6\% & 618.90 & 0.30 \\
+RandomForest (T+E) & 1213${}_{-22,+26}$ & 0.107 & 26.2 & 14.3 & 0 & 16.6\% & 323.74 & 0.74 \\
+LightGBM (D) & 1197${}_{-30,+28}$ & 0.076 & 26.9 & 23.5 & 0 & 15.4\% & 1.79 & 0.12 \\
+ExtraTrees (T) & 1196${}_{-34,+25}$ & 0.079 & 27.0 & 17.1 & 0 & 17.2\% & 189.76 & 0.08 \\
+RandomForest (T) & 1160${}_{-34,+35}$ & 0.076 & 28.6 & 16.4 & 0 & 17.8\% & 323.74 & 0.08 \\
+TorchMLP (D) & 1081${}_{-27,+24}$ & 0.021 & 31.9 & 29.1 & 0 & 19.3\% & 6.83 & 0.15 \\
+FastaiMLP (D) & 1052${}_{-33,+29}$ & 0.026 & 33.1 & 29.9 & 0 & 22.1\% & 2.91 & 0.37 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.011 & 34.8 & 32.8 & 0 & 24.1\% & 0.38 & 0.04 \\
+Linear (T+E) & 998${}_{-37,+25}$ & 0.042 & 34.9 & 25.5 & 0 & 29.2\% & 51.79 & 0.22 \\
+Linear (T) & 962${}_{-28,+24}$ & 0.026 & 36.0 & 30.5 & 0 & 30.1\% & 51.79 & 0.08 \\
+Linear (D) & 951${}_{-29,+27}$ & 0.019 & 36.3 & 27.7 & 0 & 31.1\% & 1.61 & 0.10 \\
+ExtraTrees (D) & 915${}_{-30,+32}$ & 0.010 & 37.2 & 34.3 & 0 & 26.7\% & 0.25 & 0.04 \\
+KNN (T+E) & 687${}_{-45,+40}$ & 0.000 & 41.7 & 41.4 & 0 & 48.5\% & 3.57 & 0.19 \\
+KNN (T) & 604${}_{-44,+49}$ & 0.000 & 42.7 & 42.5 & 0 & 50.3\% & 3.57 & 0.04 \\
+KNN (D) & 462${}_{-71,+70}$ & 0.000 & 43.9 & 43.7 & 0 & 58.7\% & 0.07 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/full-imputed-cls/tabarena_leaderboard.csv b/data/full-imputed-cls/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..8387fe09f12392be3b2fb1fc5b405e2964554ed0
--- /dev/null
+++ b/data/full-imputed-cls/tabarena_leaderboard.csv
@@ -0,0 +1,46 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+AutoGluon 1.3 (4h),7918.9967654708535,21.9962719316371,2834.6060407147975,3.0873029091200266,0.43572604440948826,0.43087386935667604,0.06891082959979876,0.03901043077138905,31011.170971980166,263.893057970457,9.68421052631579,0.159155,7086.632828738954,3.4718479580349393,1322.7174946761893,2.35500290690449,0.3593825434326649,0.428274830438652,0.03226297629046343,0.018303111934730745,23372.022224152715,142.27612475951975,6.5,6,3,1,28,1570.2,29.8,31.0,0.8026315789473685,0.3000744837987672
+TABM (tuned + ensemble),34341.376551290836,9.60910518720136,4223.58680397795,3.4317121842487635,0.5077918677314276,0.529397806419036,0.082399577608605,0.058005296379994874,45864.929770826835,134.55326674312073,9.697368421052632,0.17047,8245.070318195554,2.6588550435172187,2466.2108648716858,1.5022465694891438,0.47136645123500964,0.5457081140897149,0.0374695276183053,0.02990990162448854,39661.48674821285,121.43882105497326,8.0,3,2,2,31,1569.7,27.6,28.1,0.8023325358851675,0.22185652754538204
+GBM (tuned + ensemble),2957.8209862183407,11.98416593290909,759.1477152793035,2.5417865978850473,0.5957286671834613,0.6001432201748355,0.09787392914230995,0.058771855309898056,8568.794143617388,184.93302208876983,11.105263157894736,0.16698000000000002,1552.8742877377404,3.4757837878333198,382.05361557599804,1.4876036641335277,0.6251845339504845,0.6321931999484582,0.05068192653455761,0.02199552512560315,7514.342598912193,103.35126359750211,10.5,1,1,3,33,1533.5,28.5,29.1,0.7703349282296651,0.15791570680263833
+REALMLP (tuned + ensemble),75228.3606640392,17.6439790335315,5880.304462786179,8.382833590222491,0.5811880944579212,0.5735730659101549,0.09241691273181556,0.05324352029880537,134045.19581697704,292.40009500207566,11.18421052631579,0.166985,21596.535681260957,5.895895957946777,5534.7676914745925,3.874032586779933,0.5453504649932683,0.5623887498677591,0.04983329446999113,0.029565742001882556,90524.58108841689,250.02806627683862,9.0,0,0,3,35,1530.9,30.9,30.0,0.7685406698564593,0.1300254201050957
+TABICL (default),107.96023476381747,19.280086713506464,9.625347263659664,2.230965763361112,0.49900795570726975,0.5465023740990022,0.07942229531219994,0.05664735640934706,168.76491644336255,230.09998232516136,11.43421052631579,0.17207,25.732762111557854,3.449363695250617,8.684246340890724,1.7433667301105085,0.5497919378000328,0.567554590317891,0.03858333399786745,0.018532235328146802,137.81489160855577,127.65482709424812,10.0,6,4,1,27,1525.0,21.6,30.2,0.7628588516746412,0.28522470585642895
+TABM (tuned),34341.376551290836,1.0683778852050068,4223.58680397795,0.37888409513825755,0.5928893193157706,0.5852501756907074,0.09275955631038968,0.07202518870644577,45864.929770826835,13.890136033418576,13.197368421052632,0.172585,8245.070318195554,0.2723346948623657,2466.2108648716858,0.17557376557934842,0.5877464635288101,0.634980455939971,0.04800849593484757,0.03209545871697756,39661.48674821285,10.546435553124088,13.75,1,3,2,32,1484.0,28.0,29.4,0.7227870813397129,0.15576586816238858
+CAT (tuned + ensemble),16504.07579820414,2.581779239609925,3201.0265707590415,0.8915752484646539,0.6209138650557025,0.6100271864514402,0.09115568143278008,0.05171286523442811,27193.868813721343,44.08651163500922,13.56578947368421,0.16040500000000002,5046.900271190538,1.2630292971928916,1372.9411122807264,0.5562989902961428,0.6153632608570143,0.669054345099479,0.05787378892595674,0.023358363194257874,20541.46486167592,37.76547447057217,12.5,0,1,2,35,1475.5,23.8,25.7,0.7144138755980861,0.11335488880780162
+CAT (tuned),16504.07579820414,0.42638187025025576,3201.0265707590415,0.12024042770987867,0.6346801229162512,0.6217367947492924,0.0933016487821738,0.051383766225333326,27193.868813721343,6.229177098348047,13.828947368421053,0.161705,5046.900271190538,0.12877851062350804,1372.9411122807264,0.07385371803542701,0.6314852452199684,0.6847359221621947,0.05645008601114826,0.027822328565894415,20541.46486167592,4.9725379405984995,13.5,1,2,1,34,1467.9,22.9,28.9,0.708433014354067,0.14338794640411837
+GBM (tuned),2957.8209862183407,1.875632255676894,759.1477152793035,0.5049369881097493,0.6821201235665096,0.6578465553035225,0.10551186244180051,0.06848969819422726,8568.794143617388,32.37091249654915,14.276315789473685,0.16877999999999999,1552.8742877377404,0.5045170254177518,382.05361557599804,0.2538713244201605,0.7168421399910502,0.6818100706684505,0.05207128139882822,0.031112468640158004,7514.342598912193,15.175591971121808,13.0,0,0,0,38,1460.5,28.5,27.8,0.6982655502392344,0.08323051382797168
+XGB (tuned + ensemble),5957.374200563333,6.456417350253166,1167.526219278486,2.7907218103162132,0.6794057655741552,0.6681192465092817,0.10670226326209542,0.06938954053725854,10832.944520166355,137.58836217438702,14.68421052631579,0.165745,1680.0658507664998,2.2809726662105985,685.86510540535,1.4547593315263065,0.7126419091535116,0.7253164483987984,0.060361169858999864,0.027448154499449852,8251.297786111467,74.47019952683146,13.5,0,1,1,36,1450.8,27.4,27.3,0.6889952153110048,0.10802493613607358
+CAT (default),220.2627424415789,0.2710267032099049,109.9732905896213,0.13662992734425208,0.6551829671744798,0.6560274611111252,0.10281876446924927,0.055251622712656184,404.9759898295662,6.476751807794606,14.710526315789474,0.16373,18.264583627382912,0.17750852637820774,5.723546572951673,0.0761539571798428,0.6872314742824219,0.6661744510015815,0.05481736123368691,0.025874497523709752,103.69107151573829,5.120402547941957,16.0,1,3,1,33,1450.2,24.8,26.2,0.6883971291866029,0.14001756263385653
+TABPFNV2 (tuned + ensemble),10656.543301447302,101.4355180454533,2744.2670001010792,44.749282859570734,0.5138103973952525,0.5539076178208143,0.09695044510113665,0.08124536736386809,49872.05341476571,3444.4876051657507,14.723684210526315,0.17261500000000002,3494.967008225123,12.760541562239329,3008.2157047151595,20.848616639963154,0.5072451025993939,0.5739389565251636,0.04061436251124223,0.031106953519191773,28624.579287895787,825.5109643363919,9.5,8,4,1,25,1449.9,25.2,31.9,0.6880980861244019,0.32648046139872094
+MNCA (tuned),57356.284085895306,20.170870465214488,5990.817791505437,2.0485901061394993,0.721681429841744,0.6606257243361955,0.10501593687511648,0.07242005802280899,80456.91211763977,129.4389431731015,16.513157894736842,0.17054999999999998,14186.536935488384,0.6020842525694106,4879.890404506269,0.5247194359730172,0.7744612480983963,0.6781072751281167,0.06755654915093501,0.04344719805270737,66956.02864547497,27.735396922747526,15.5,1,0,0,37,1411.7,24.2,30.4,0.6474282296650717,0.10323935747363282
+MNCA (tuned + ensemble),57356.284085895306,531.7593351699455,5990.817791505437,50.07821547982156,0.6269167185273185,0.6106049466828749,0.10389701842794222,0.0798115271970102,80456.91211763977,3301.6213661597712,16.57894736842105,0.183035,14186.536935488384,14.17156207561493,4879.890404506269,8.743516387788919,0.6265388929939845,0.5779679694371859,0.06497465354642928,0.03974745118482769,66956.02864547497,548.1975258046007,12.5,0,2,4,32,1410.1,25.6,29.3,0.645933014354067,0.13333696320265415
+XGB (tuned),5957.374200563333,1.301870340352867,1167.526219278486,0.6678561539347552,0.7344435643624297,0.7080657004391169,0.11080653994457279,0.07384264693213777,10832.944520166355,28.47499330573137,16.63157894736842,0.16848000000000002,1680.0658507664998,0.3827125522825453,685.86510540535,0.2050912539994952,0.7444722954009122,0.7540446689272955,0.06946524781463731,0.034107460094903265,8251.297786111467,11.461364611937853,15.0,0,0,0,38,1408.1,29.3,26.0,0.6447368421052632,0.0754804237905414
+TABPFNV2 (tuned),10656.543301447302,3.410806728176206,2744.2670001010792,1.555769686886014,0.6207514015330685,0.635473706243979,0.1213068605022819,0.09569674055075465,49872.05341476571,114.00749994252256,17.82894736842105,0.1868,3494.967008225123,0.5060818235079447,3008.2157047151595,0.5144277113236544,0.7315480103218319,0.6473273087383917,0.08561151707807774,0.042273708446926114,28624.579287895787,25.570859321617846,13.0,1,8,1,28,1384.2,33.6,28.4,0.6175239234449761,0.19430531047345106
+TABM (default),150.0762373017289,1.2507152029645372,19.886819500646006,0.46487430096802723,0.7289050360420961,0.7331300276199935,0.1257035609004299,0.09205634091522938,189.61145131944258,14.042337078320946,18.30263157894737,0.17246,31.126562476158142,0.20260944763819377,10.213381764059356,0.1381032773929915,0.8404844924976476,0.7991898878806922,0.06154427249600947,0.03281092722346347,144.96049255349743,10.968725907290855,18.0,0,0,0,38,1372.7,29.7,27.0,0.6067583732057417,0.08288591058268369
+TABPFNV2 (default),11.400364582092442,0.8958970822786029,4.227300497658887,0.4575723059305235,0.6515996652210705,0.6914396497170463,0.1303285119303074,0.10434015301735906,53.73712299744585,29.471362460513575,18.842105263157894,0.1886,7.994279013739691,0.2908047080039978,3.368600991426515,0.3152861168047789,0.782945687586422,0.7254516789049502,0.07929605187577571,0.04313507441518306,41.34560285308973,17.75894630310718,17.0,4,1,4,29,1363.4,26.2,31.3,0.5944976076555024,0.21091254835613285
+NN_TORCH (tuned + ensemble),24331.947126566527,16.31368946276213,3050.9102763481856,4.325404104362444,0.77831117981961,0.7537646164336206,0.1163781574015359,0.08020239339635844,56038.718047349466,227.24142067178357,19.210526315789473,0.17071999999999998,9097.789536105262,5.056680162747702,2389.2199648500327,2.157502904371376,0.9031626632034824,0.8115035227779454,0.06782483230561193,0.04484844246885637,44480.90343297912,173.17543399472862,20.0,0,0,0,38,1358.1,28.5,25.2,0.5861244019138756,0.06710526855854969
+REALMLP (tuned),75228.3606640392,0.8387730677922566,5880.304462786179,0.4572085739395715,0.7977234575542793,0.7425716110334656,0.12348440991003401,0.0833370259997771,134045.19581697704,13.981658721822997,19.31578947368421,0.16874,21596.535681260957,0.2674341731601291,5534.7676914745925,0.19066645138299287,0.8787741747777617,0.7462218843777739,0.07816480714694801,0.04156599671061481,90524.58108841689,11.215626382435188,17.0,0,0,0,38,1354.0,30.3,26.4,0.583732057416268,0.06571086364796647
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+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+AutoGluon 1.3 (4h) & \textcolor{gold}{\textbf{1806${}_{-61,+56}$}} & \textcolor{gold}{\textbf{0.735}} & \textcolor{gold}{\textbf{4.5}} & \textcolor{silver}{\textbf{2.5}} & 2 & \textcolor{silver}{\textbf{2.7\%}} & 1625.74 & 6.76 \\
+RealMLP (T+E) & \textcolor{silver}{\textbf{1747${}_{-53,+70}$}} & \textcolor{silver}{\textbf{0.734}} & \textcolor{silver}{\textbf{5.5}} & 4.1 & 0 & \textcolor{gold}{\textbf{2.0\%}} & 6567.16 & 2.48 \\
+ModernNCA (T+E) & \textcolor{bronze}{\textbf{1628${}_{-59,+57}$}} & 0.603 & \textcolor{bronze}{\textbf{8.4}} & \textcolor{bronze}{\textbf{2.8}} & \textcolor{silver}{\textbf{3}} & 3.8\% & 3779.52 & 7.69 \\
+TabDPT (D) & 1618${}_{-57,+77}$ & \textcolor{bronze}{\textbf{0.635}} & 8.8 & \textcolor{gold}{\textbf{2.3}} & \textcolor{gold}{\textbf{5}} & \textcolor{bronze}{\textbf{2.9\%}} & 22.53 & 8.55 \\
+LightGBM (T+E) & 1610${}_{-61,+57}$ & 0.531 & 9.1 & 7.1 & 0 & 5.0\% & 700.15 & 9.32 \\
+CatBoost (T+E) & 1608${}_{-50,+65}$ & 0.536 & 9.0 & 7.2 & 0 & 4.5\% & 3552.96 & 0.97 \\
+CatBoost (T) & 1576${}_{-54,+53}$ & 0.512 & 10.3 & 6.9 & 0 & 4.6\% & 3552.96 & 0.10 \\
+TabM (T+E) & 1568${}_{-56,+48}$ & 0.488 & 10.5 & 6.5 & 0 & 3.3\% & 4158.29 & 1.41 \\
+XGBoost (T+E) & 1500${}_{-46,+50}$ & 0.432 & 12.8 & 12.2 & 0 & 5.5\% & 834.93 & 2.61 \\
+LightGBM (T) & 1499${}_{-63,+57}$ & 0.432 & 12.8 & 10.8 & 0 & 5.6\% & 700.15 & 0.97 \\
+XGBoost (T) & 1481${}_{-47,+56}$ & 0.398 & 13.6 & 13.0 & 0 & 5.6\% & 834.93 & 0.39 \\
+ModernNCA (T) & 1449${}_{-60,+50}$ & 0.351 & 14.6 & 6.7 & 0 & 5.9\% & 3779.52 & 0.40 \\
+CatBoost (D) & 1438${}_{-49,+53}$ & 0.389 & 15.1 & 11.6 & 0 & 6.2\% & 10.89 & 0.09 \\
+TabM (T) & 1438${}_{-53,+55}$ & 0.380 & 15.2 & 11.7 & 0 & 4.3\% & 4158.29 & 0.17 \\
+RealMLP (T) & 1395${}_{-53,+82}$ & 0.344 & 16.5 & 13.3 & 0 & 4.6\% & 6567.16 & 0.09 \\
+TabPFNv2 (T+E) & 1378${}_{-65,+56}$ & 0.408 & 17.3 & 3.0 & \textcolor{silver}{\textbf{3}} & 5.1\% & 4223.87 & 27.54 \\
+ModernNCA (D) & 1342${}_{-40,+53}$ & 0.209 & 18.8 & 13.4 & 0 & 7.4\% & 15.50 & 0.30 \\
+TabM (D) & 1329${}_{-55,+52}$ & 0.287 & 19.4 & 15.6 & 0 & 6.0\% & 13.32 & 0.13 \\
+TorchMLP (T+E) & 1303${}_{-53,+58}$ & 0.171 & 20.5 & 14.7 & 0 & 7.6\% & 4608.59 & 1.23 \\
+TabPFNv2 (T) & 1298${}_{-61,+53}$ & 0.282 & 20.8 & 7.6 & 0 & 6.2\% & 4223.87 & 0.45 \\
+RealMLP (D) & 1284${}_{-48,+58}$ & 0.137 & 21.3 & 18.2 & 0 & 7.1\% & 33.36 & 0.08 \\
+ExtraTrees (T+E) & 1273${}_{-51,+55}$ & 0.157 & 21.7 & 14.3 & 0 & 10.0\% & 158.22 & 0.84 \\
+LightGBM (D) & 1261${}_{-50,+49}$ & 0.070 & 22.1 & 21.5 & 0 & 8.1\% & 2.11 & 0.27 \\
+ExtraTrees (T) & 1254${}_{-49,+53}$ & 0.129 & 22.5 & 16.9 & 0 & 10.3\% & 158.22 & 0.15 \\
+TabPFNv2 (D) & 1230${}_{-61,+50}$ & 0.228 & 23.5 & 10.1 & 0 & 7.6\% & 2.80 & 0.31 \\
+TorchMLP (T) & 1224${}_{-52,+55}$ & 0.123 & 23.6 & 20.3 & 0 & 8.4\% & 4608.59 & 0.10 \\
+XGBoost (D) & 1217${}_{-46,+51}$ & 0.111 & 24.1 & 21.4 & 0 & 8.8\% & 2.24 & 0.24 \\
+RandomForest (T+E) & 1206${}_{-51,+43}$ & 0.066 & 24.5 & 22.4 & 0 & 10.9\% & 515.73 & 0.77 \\
+RandomForest (T) & 1153${}_{-47,+47}$ & 0.048 & 26.4 & 24.7 & 0 & 11.4\% & 515.73 & 0.12 \\
+EBM (T+E) & 1120${}_{-47,+61}$ & 0.160 & 27.6 & 13.5 & 0 & 13.7\% & 1890.68 & 0.13 \\
+ExtraTrees (D) & 1107${}_{-56,+48}$ & 0.061 & 28.3 & 25.1 & 0 & 12.2\% & 0.47 & 0.06 \\
+EBM (T) & 1083${}_{-60,+55}$ & 0.137 & 29.2 & 17.2 & 0 & 14.2\% & 1890.68 & 0.01 \\
+FastaiMLP (T+E) & 1039${}_{-46,+56}$ & 0.023 & 30.5 & 28.4 & 0 & 12.2\% & 540.06 & 2.67 \\
+TorchMLP (D) & 1037${}_{-57,+53}$ & 0.016 & 30.6 & 28.4 & 0 & 11.9\% & 20.48 & 0.08 \\
+EBM (D) & 1019${}_{-59,+58}$ & 0.093 & 31.2 & 28.2 & 0 & 15.1\% & 6.33 & 0.04 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.000 & 31.8 & 31.2 & 0 & 12.9\% & 0.53 & 0.06 \\
+TabICL (D) & 996${}_{-54,+45}$ & 0.000 & 31.8 & 31.2 & 0 & 12.9\% & 0.53 & 0.06 \\
+FastaiMLP (T) & 984${}_{-56,+46}$ & 0.013 & 32.3 & 30.9 & 0 & 12.7\% & 540.06 & 0.32 \\
+FastaiMLP (D) & 852${}_{-69,+56}$ & 0.000 & 35.7 & 35.0 & 0 & 17.1\% & 2.60 & 0.39 \\
+KNN (T+E) & 520${}_{-88,+72}$ & 0.000 & 40.7 & 40.5 & 0 & 36.1\% & 2.43 & 0.14 \\
+Linear (T+E) & 469${}_{-88,+60}$ & 0.000 & 41.3 & 41.2 & 0 & 35.4\% & 45.74 & 0.11 \\
+KNN (T) & 423${}_{-88,+102}$ & 0.000 & 41.7 & 41.5 & 0 & 36.8\% & 2.43 & 0.03 \\
+Linear (T) & 407${}_{-75,+72}$ & 0.000 & 41.9 & 41.8 & 0 & 35.6\% & 45.74 & 0.05 \\
+Linear (D) & 280${}_{-95,+75}$ & 0.000 & 43.2 & 43.1 & 0 & 38.1\% & 1.19 & 0.09 \\
+KNN (D) & 215${}_{-120,+122}$ & 0.000 & 43.6 & 43.4 & 0 & 40.8\% & 0.04 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/full-imputed-reg/tabarena_leaderboard.csv b/data/full-imputed-reg/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..97a24489a71730e7cbbc20cb26714e85f0191da9
--- /dev/null
+++ b/data/full-imputed-reg/tabarena_leaderboard.csv
@@ -0,0 +1,46 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+AutoGluon 1.3 (4h),10053.09853225386,65.70725388180496,3138.716102354025,8.201870690838728,0.26461131829744206,0.2910041255880444,0.02699587865316731,0.03762065136585477,79227.35072832079,1344.7158520024777,4.538461538461538,4.16484,12938.261539538702,10.17258334159851,1625.738447135909,6.759745919437983,0.23133371369265834,0.295652367761122,0.015883109174547605,0.016643068102528722,85166.92529682687,517.5224332581762,3.0,2,3,2,6,1806.5,55.2,60.1,0.9195804195804196,0.39930069930069934
+REALMLP (tuned + ensemble),67709.67308180964,12.011677133731354,6963.87401913588,6.973854948645224,0.26596078198184286,0.30562769026149705,0.020013239864383952,0.02890399380839934,303258.6729720159,231.34066267352048,5.538461538461538,4.12234,28517.58209461636,4.232689115736219,6567.164030631503,2.480181700638205,0.19393332805279354,0.3339396668324287,0.017045117638759044,0.024876069447861582,203710.74184331874,227.32654052633927,4.0,0,1,3,9,1746.9,69.9,52.6,0.8968531468531469,0.24096736596736598
+MNCA (tuned + ensemble),31759.101002019288,48.99773550746787,6166.048475961011,17.643273328787647,0.39661026996847265,0.37338402309071167,0.03809303342780051,0.03729025347559432,186080.1526868885,677.6872983460363,8.384615384615385,4.48697,16310.556293937894,10.227302259869045,3779.5248398651206,7.690422738079533,0.35096562881515253,0.3553220810641804,0.019057076597787104,0.02404483039332963,198038.2195884601,507.5335424076248,7.0,3,1,1,8,1628.3,56.7,58.6,0.8321678321678322,0.3570428109901794
+TABDPT (default),150.02111775732448,54.38835432835114,28.728808967170515,25.042434382939593,0.36524485788247774,0.3767809526402384,0.029011919452666072,0.03451421977602538,859.9949957462201,1916.4746059233426,8.846153846153847,4.26747,137.4807067182329,29.239719518025716,22.52690614988171,8.547073882575031,0.28825222902504594,0.3462761515522977,0.024988993973347418,0.022011234994563023,904.9754515353668,1821.0928942426492,8.0,5,0,0,8,1618.2,76.9,56.6,0.8216783216783217,0.44424741924741923
+CAT (tuned + ensemble),30302.612278386874,4.803950041991014,6846.603031359736,1.23549623448159,0.46394270088709505,0.482144954832304,0.04475905931200346,0.05586568212598891,150000.99966111325,116.73562230823504,9.0,4.20863,22090.5574801498,2.685193909539117,3552.958864906998,0.9657383741190037,0.4150140849407447,0.48332107057126966,0.021491024872657194,0.036001021424025365,164350.82166574517,80.04598787510639,9.0,0,0,1,12,1608.1,64.2,49.2,0.8181818181818182,0.1387461503858265
+GBM (tuned + ensemble),3469.6354441180188,51.50608464469258,806.792675338677,8.57889161766087,0.4687885136784251,0.48192601834426685,0.049628720183150093,0.06009435650772031,20529.34803212188,1135.938430886033,9.076923076923077,4.21165,3055.419878217909,17.84196005927192,700.1537746143185,9.321818212785747,0.41525976638432377,0.4124957657121928,0.02669651687138408,0.03428335611022812,21786.570035668443,556.5446710812895,7.0,0,0,0,13,1609.9,56.1,60.6,0.8164335664335665,0.1408062697536382
+CAT (tuned),30302.612278386874,0.5550543483505901,6846.603031359736,0.16909210735792113,0.48768370669415817,0.5006635065398762,0.04596154257880672,0.05949308403285739,150000.99966111325,13.40589862804556,10.26923076923077,4.23064,22090.5574801498,0.3789627022213406,3552.958864906998,0.10450043094654878,0.5120020111687084,0.49061208598211514,0.026006470182449948,0.03741223775007456,164350.82166574517,10.277501137463553,10.0,0,1,1,11,1575.6,52.6,53.3,0.7893356643356644,0.14471447356062742
+TABM (tuned + ensemble),42663.03407314721,6.401903104782104,7337.2216133773,2.4450322937588758,0.5116630985629478,0.5032977898254283,0.032854613342711655,0.07923986201524974,180683.6213219068,120.23686774247648,10.538461538461538,4.1458,20820.912871148852,3.0152715841929116,4158.291053548661,1.4096720886484275,0.43090407292838107,0.4272044906933773,0.031023728849073895,0.02730307361680199,166291.5239595113,107.26839105814781,6.0,0,0,1,12,1568.0,47.3,55.2,0.7832167832167832,0.15363693425074498
+XGB (tuned + ensemble),6384.208144447945,12.513759183476113,1409.0815132938305,4.086239880001324,0.567688570586379,0.5725638306726402,0.055019280156632426,0.0676716205271066,26398.72664659111,353.2622484982949,12.76923076923077,4.22219,2596.930354913076,6.618133616447449,834.9300717202715,2.614265349176195,0.6256631137377601,0.6169840519555478,0.03390767076571366,0.04797658607386891,26370.57574541831,164.97865636299682,13.0,0,0,0,13,1500.2,49.4,45.3,0.7325174825174825,0.08187865427684432
+GBM (tuned),3469.6354441180188,7.385669826034806,806.792675338677,1.273710690539978,0.5677497712335507,0.5646962660086592,0.05572937722518249,0.07273694393515719,20529.34803212188,170.85098129275468,12.846153846153847,4.23482,3055.419878217909,2.690451833936903,700.1537746143185,0.9682498776019389,0.5552965190223861,0.535641070170376,0.027972044011269404,0.03922298758674819,21786.570035668443,130.77892053638826,12.0,0,0,0,13,1498.7,57.0,62.8,0.7307692307692307,0.09274030393939896
+XGB (tuned),6384.208144447945,2.5463058025409016,1409.0815132938305,0.7958717570786976,0.6023900941776976,0.599191708641876,0.05649722265542322,0.07175664058011187,26398.72664659111,74.35901041305158,13.576923076923077,4.23559,2596.930354913076,1.7797584003872342,834.9300717202715,0.3883258596259137,0.6620816436761664,0.6377270710295441,0.03317270939304562,0.04927060675426248,26370.57574541831,37.05707269936294,12.0,0,0,0,13,1481.0,55.7,46.3,0.7141608391608392,0.07698877919466154
+MNCA (tuned),31759.101002019288,2.180037997319148,6166.048475961011,0.7699041848491119,0.6494157477338965,0.5928326047970892,0.05893528444168654,0.07225555938838021,186080.1526868885,32.66971509741366,14.615384615384615,4.74895,16310.556293937894,0.48738079600863987,3779.5248398651206,0.3958729871859153,0.686862000079896,0.6299271517750961,0.027829209062692706,0.05013010527584701,198038.2195884601,22.639832589759934,17.0,0,2,1,10,1449.4,49.9,59.3,0.6905594405594405,0.1501631920773124
+CAT (default),101.69209800418625,0.27084102752881173,23.09400681118272,0.13269262448628016,0.6107641551712003,0.6218727584243557,0.062460236998275034,0.07708318639558216,496.3567653671193,9.26273127760114,15.115384615384615,4.21395,62.699359814325966,0.2736650307973226,10.889876924735699,0.09199146861016888,0.5847419575275037,0.6170852848140443,0.030438989353365198,0.03712451320809096,412.72250349454265,8.72239577968297,13.0,0,0,0,13,1437.7,52.1,48.9,0.6791958041958042,0.08584535972840124
+TABM (tuned),42663.03407314721,0.6790118826760185,7337.2216133773,0.2826346699151617,0.6195303355392976,0.6016841017485475,0.04291459493786008,0.10009773912606208,180683.6213219068,13.142332198971314,15.153846153846153,4.26932,20820.912871148852,0.3543446593814426,4158.291053548661,0.16724776809008424,0.5658117450288995,0.5524525384273968,0.039608740417227284,0.03536573040794217,166291.5239595113,12.524825096296247,13.0,0,0,0,13,1437.7,54.8,52.3,0.6783216783216783,0.08529071847502581
+REALMLP (tuned),67709.67308180964,0.5510838443397457,6963.87401913588,0.35676328285131836,0.655870427194709,0.6176644486616331,0.04609702039794716,0.0810536599656669,303258.6729720159,12.866326813784351,16.53846153846154,4.2931,28517.58209461636,0.2134650813208686,6567.164030631503,0.09160214336768219,0.67949922992921,0.6664835865428769,0.043563415018409435,0.04951944914046118,203710.74184331874,10.089309852682138,17.0,0,0,0,13,1395.2,81.1,52.3,0.6468531468531469,0.07544811900015519
+TABPFNV2 (tuned + ensemble),9087.265872733002,78.03043969614893,3585.89762046501,56.25472027093109,0.5916274908500255,0.6393456642784099,0.050662115797136684,0.0930896683951232,105571.67881602701,4530.894138806997,17.346153846153847,4.11819,4897.1321855386095,13.798940539360046,4223.8673583405725,27.542795487744606,0.7821381201281035,0.7702280073934004,0.034084501561638336,0.03467029575770307,99491.49864758716,2157.1863969834576,19.0,3,2,0,8,1378.5,55.3,64.7,0.6284965034965035,0.33194312310617097
+MNCA (default),99.58075828980176,2.0463109099966847,18.34482305470451,0.7039566701063034,0.7905734390801098,0.7438997183128557,0.07422761695620157,0.09797180414713179,547.7971597202185,27.012858819060174,18.846153846153847,4.94033,49.107323222690155,0.41290783882141113,15.50085128260229,0.29868905742963153,0.9481480616823943,0.8801463886825595,0.03934956674595991,0.09658469319813351,536.5510136544884,15.897990578305649,18.0,0,0,0,13,1341.9,52.5,39.8,0.5944055944055944,0.07456434869101838
+TABM (default),109.08048485323914,0.5565733836247371,30.11061011517182,0.2424694191265854,0.7132806572850755,0.709857402946311,0.06035758501914987,0.13133163540831894,580.6229196238922,9.300928058329742,19.384615384615383,4.27071,65.0270922978719,0.1736939483218723,13.315938751381204,0.13061717308512005,0.6984407490367052,0.6451541074247529,0.05131334419415079,0.05222355068579704,354.8698900002396,7.016702417594013,18.0,0,0,0,13,1328.8,51.7,54.6,0.5821678321678322,0.06409168239581474
+NN_TORCH (tuned + ensemble),44960.17932087947,4.03415755573501,5714.412959426929,1.3298489659738517,0.8294336223884518,0.8020265214949197,0.07610055939510828,0.12013497439285538,220430.49479235077,97.5953017172201,20.53846153846154,4.65351,15497.21247045199,2.8239229255252414,4608.594420268999,1.2325370779691025,0.9419042422995463,0.8805586694349655,0.05961664534372901,0.0834039942118561,177697.9133637009,96.53385472639289,21.0,0,0,0,13,1302.7,57.6,52.9,0.5559440559440559,0.0679103711813622
+TABPFNV2 (tuned),9087.265872733002,3.794137261464045,3585.89762046501,1.977808999126841,0.7178550513336984,0.7399865456800228,0.0618004152523972,0.11851504476590419,105571.67881602701,193.9605838613745,20.807692307692307,4.17059,4897.1321855386095,0.8012150287628174,4223.8673583405725,0.45159866325593434,1.0,1.0,0.05688219535982175,0.06558436878663254,99491.49864758716,19.472884837488856,29.0,0,2,0,11,1298.3,52.4,60.1,0.5498251748251748,0.13197410518116387
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+XT (tuned + ensemble),1206.5194327423715,3.367576224375994,442.36567464742467,1.069316176291616,0.8432098887288747,0.8458357989233521,0.10048529236024564,0.12077554715910149,8220.916376158188,88.72398178178156,21.692307692307693,5.04794,766.4287914435068,3.576531834072537,158.22496863160976,0.8436571643025992,1.0,0.9967778446932313,0.049883734447319306,0.1226940033261134,5379.1926370125775,93.77443700336585,25.0,0,0,0,13,1273.2,54.3,51.0,0.5297202797202797,0.06984441103315876
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diff --git a/data/full-imputed/leaderboard.tex b/data/full-imputed/leaderboard.tex
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+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+AutoGluon 1.3 (4h) & \textcolor{gold}{\textbf{1588${}_{-27,+24}$}} & \textcolor{gold}{\textbf{0.608}} & \textcolor{gold}{\textbf{8.4}} & \textcolor{silver}{\textbf{3.1}} & \textcolor{silver}{\textbf{8}} & \textcolor{gold}{\textbf{5.8\%}} & 1408.78 & 3.34 \\
+RealMLP (T+E) & \textcolor{silver}{\textbf{1547${}_{-21,+26}$}} & \textcolor{silver}{\textbf{0.499}} & \textcolor{silver}{\textbf{9.7}} & 6.3 & 0 & \textcolor{bronze}{\textbf{7.4\%}} & 6044.76 & 3.80 \\
+TabM (T+E) & \textcolor{bronze}{\textbf{1540${}_{-21,+29}$}} & \textcolor{bronze}{\textbf{0.491}} & \textcolor{bronze}{\textbf{9.9}} & 4.9 & 3 & \textcolor{silver}{\textbf{7.0\%}} & 3285.87 & 1.47 \\
+LightGBM (T+E) & 1525${}_{-24,+24}$ & 0.437 & 10.6 & 6.5 & 1 & 8.6\% & 416.98 & 2.64 \\
+CatBoost (T+E) & 1482${}_{-27,+16}$ & 0.419 & 12.4 & 8.3 & 0 & 7.9\% & 1658.41 & 0.65 \\
+CatBoost (T) & 1469${}_{-23,+19}$ & 0.403 & 12.9 & 7.0 & 1 & 8.1\% & 1658.41 & 0.08 \\
+TabM (T) & 1450${}_{-26,+21}$ & 0.400 & 13.7 & 7.3 & 1 & 8.0\% & 3285.87 & 0.17 \\
+LightGBM (T) & 1447${}_{-28,+21}$ & 0.347 & 13.9 & 11.7 & 0 & 9.3\% & 416.98 & 0.33 \\
+XGBoost (T+E) & 1439${}_{-23,+19}$ & 0.349 & 14.2 & 9.9 & 0 & 9.4\% & 693.49 & 1.69 \\
+ModernNCA (T+E) & 1434${}_{-28,+24}$ & 0.432 & 14.5 & 5.3 & 3 & 8.7\% & 4621.67 & 8.15 \\
+CatBoost (D) & 1427${}_{-21,+26}$ & 0.356 & 14.8 & 7.9 & 1 & 9.3\% & 6.83 & 0.08 \\
+TabPFNv2 (T+E) & 1413${}_{-26,+27}$ & 0.466 & 15.4 & \textcolor{gold}{\textbf{3.0}} & \textcolor{gold}{\textbf{11}} & 8.5\% & 3030.15 & 21.44 \\
+XGBoost (T) & 1403${}_{-23,+21}$ & 0.299 & 15.9 & 13.2 & 0 & 9.7\% & 693.49 & 0.31 \\
+ModernNCA (T) & 1401${}_{-21,+18}$ & 0.297 & 16.0 & 8.7 & 1 & 9.3\% & 4621.67 & 0.47 \\
+TabICL (D) & 1386${}_{-23,+25}$ & 0.373 & 16.6 & \textcolor{bronze}{\textbf{4.5}} & 6 & 9.2\% & 6.63 & 1.48 \\
+RealMLP (T) & 1347${}_{-23,+24}$ & 0.238 & 18.6 & 14.7 & 0 & 10.4\% & 6044.76 & 0.18 \\
+TabPFNv2 (T) & 1346${}_{-25,+27}$ & 0.354 & 18.6 & 5.6 & 1 & 10.6\% & 3030.15 & 0.46 \\
+TabM (D) & 1345${}_{-18,+21}$ & 0.275 & 18.6 & 12.8 & 0 & 10.9\% & 10.49 & 0.13 \\
+TorchMLP (T+E) & 1327${}_{-22,+25}$ & 0.209 & 19.5 & 14.9 & 0 & 10.6\% & 2874.67 & 1.95 \\
+TabPFNv2 (D) & 1319${}_{-22,+22}$ & 0.318 & 20.0 & 5.5 & 4 & 11.6\% & 3.36 & 0.31 \\
+TabDPT (D) & 1299${}_{-28,+24}$ & 0.293 & 21.0 & 4.9 & \textcolor{bronze}{\textbf{7}} & 12.3\% & 22.53 & 8.55 \\
+ModernNCA (D) & 1298${}_{-22,+26}$ & 0.153 & 21.0 & 12.6 & 1 & 12.8\% & 14.87 & 0.31 \\
+EBM (T+E) & 1289${}_{-25,+25}$ & 0.175 & 21.5 & 13.5 & 0 & 14.6\% & 1331.68 & 0.20 \\
+FastaiMLP (T+E) & 1247${}_{-21,+24}$ & 0.152 & 23.5 & 14.7 & 0 & 14.0\% & 593.24 & 4.47 \\
+ExtraTrees (T+E) & 1241${}_{-20,+22}$ & 0.117 & 23.8 & 16.0 & 0 & 14.3\% & 183.02 & 0.76 \\
+EBM (T) & 1235${}_{-23,+18}$ & 0.126 & 24.1 & 17.7 & 0 & 15.3\% & 1331.68 & 0.02 \\
+XGBoost (D) & 1234${}_{-24,+19}$ & 0.106 & 24.2 & 19.6 & 0 & 12.7\% & 1.94 & 0.12 \\
+RealMLP (D) & 1234${}_{-22,+21}$ & 0.102 & 24.2 & 20.2 & 0 & 12.7\% & 35.36 & 0.19 \\
+TorchMLP (T) & 1224${}_{-20,+23}$ & 0.103 & 24.6 & 21.2 & 0 & 12.7\% & 2874.67 & 0.13 \\
+LightGBM (D) & 1202${}_{-23,+23}$ & 0.074 & 25.7 & 23.0 & 0 & 13.5\% & 1.96 & 0.14 \\
+RandomForest (T+E) & 1201${}_{-24,+21}$ & 0.096 & 25.8 & 15.8 & 0 & 15.1\% & 373.18 & 0.77 \\
+EBM (D) & 1200${}_{-28,+22}$ & 0.125 & 25.8 & 13.5 & 1 & 16.2\% & 4.67 & 0.04 \\
+ExtraTrees (T) & 1200${}_{-29,+17}$ & 0.092 & 25.9 & 17.0 & 0 & 15.4\% & 183.02 & 0.09 \\
+FastaiMLP (T) & 1162${}_{-20,+23}$ & 0.070 & 27.5 & 22.8 & 0 & 15.6\% & 593.24 & 0.31 \\
+RandomForest (T) & 1152${}_{-24,+20}$ & 0.069 & 28.1 & 17.9 & 0 & 16.2\% & 373.18 & 0.09 \\
+TorchMLP (D) & 1068${}_{-23,+22}$ & 0.019 & 31.6 & 28.9 & 0 & 17.5\% & 9.99 & 0.13 \\
+FastaiMLP (D) & 1010${}_{-22,+24}$ & 0.019 & 33.7 & 31.1 & 0 & 20.8\% & 2.86 & 0.37 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.008 & 34.0 & 32.3 & 0 & 21.3\% & 0.43 & 0.05 \\
+ExtraTrees (D) & 971${}_{-29,+22}$ & 0.023 & 35.0 & 31.3 & 0 & 23.0\% & 0.25 & 0.05 \\
+Linear (T+E) & 918${}_{-20,+31}$ & 0.031 & 36.5 & 28.2 & 0 & 30.8\% & 47.49 & 0.17 \\
+Linear (T) & 882${}_{-31,+31}$ & 0.019 & 37.5 & 32.8 & 0 & 31.5\% & 47.49 & 0.07 \\
+Linear (D) & 863${}_{-30,+24}$ & 0.014 & 38.0 & 30.5 & 0 & 32.9\% & 1.52 & 0.09 \\
+KNN (T+E) & 684${}_{-32,+27}$ & 0.000 & 41.4 & 41.1 & 0 & 45.3\% & 3.26 & 0.18 \\
+KNN (T) & 607${}_{-48,+31}$ & 0.000 & 42.4 & 42.3 & 0 & 46.9\% & 3.26 & 0.04 \\
+KNN (D) & 459${}_{-47,+42}$ & 0.000 & 43.8 & 43.6 & 0 & 54.1\% & 0.05 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/full-imputed/tabarena_leaderboard.csv b/data/full-imputed/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..edb4fd31b9f5f95648743dea9f89edc4bf7ed637
--- /dev/null
+++ b/data/full-imputed/tabarena_leaderboard.csv
@@ -0,0 +1,46 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+AutoGluon 1.3 (4h),8462.98349033711,33.138286938542635,2912.1242917208747,4.391016265244401,0.39210856520445686,0.3952207974156523,0.05822662641732407,0.038656173275860704,43301.56973340033,539.3969074295995,8.372549019607844,0.2055,7367.614226023356,3.9490213659074573,1408.7828331379249,3.337414261487274,0.33725328327072535,0.34354944358388295,0.022224886416280176,0.016643068102528722,33121.993698706065,185.27028732280235,5.0,8,6,3,34,1588.0,23.3,27.0,0.8324420677361853,0.3253674406914166
+REALMLP (tuned + ensemble),73311.83245680422,16.208294235543228,6156.508467345906,8.023682171781228,0.5008360344149994,0.5052732642742225,0.07396107454992124,0.047039327271839126,177178.04293492812,276.8359259771498,9.745098039215685,0.20511,23874.98158947627,5.369593381881714,6044.758795122033,3.804109742244085,0.48215030896347355,0.5149467040415536,0.0363542362621595,0.029161376498297843,128255.5951822475,249.18630751354758,8.0,0,1,6,44,1546.6,25.8,20.4,0.8012477718360071,0.15830473963861558
+TABM (tuned + ensemble),36462.58337058756,8.791583087761158,5017.258422060137,3.1802055454964386,0.5087786520610308,0.5227448610128222,0.06977046907024002,0.06341802879682454,80230.4785975727,130.9039885664859,9.911764705882353,0.20535,8420.993191123009,2.7141049438052707,3285.8688373170553,1.4723486146222118,0.47020083976979304,0.5081840173827434,0.03250086230493732,0.027567784172117953,47614.825535817596,111.53030159160512,8.0,3,2,3,43,1540.3,28.9,20.2,0.7974598930481284,0.20446721945067065
+GBM (tuned + ensemble),3088.2834950947295,22.058380506500956,771.2925090199282,4.080656504886727,0.5633713731527658,0.5700094236297886,0.08557613078017116,0.05910896345836255,11617.562781863631,427.3461655076801,10.588235294117647,0.2112,1667.667911251386,3.7860276963975696,416.9832926671224,2.6387318875879693,0.5860060403960241,0.5844894225112219,0.04023364809387353,0.02358471585546149,9501.568897853978,118.5632719261967,10.0,1,1,3,46,1525.4,23.3,23.6,0.7820855614973262,0.15355447775093242
+CAT (tuned + ensemble),20021.3498029566,3.148214934334516,4130.291158755297,0.9792413821552454,0.580901607522528,0.5774297548622486,0.07932909148042525,0.05277142640286518,58497.64726501732,62.60491239485109,12.401960784313726,0.21079,7127.110804247856,1.3727677133348253,1658.412974283854,0.65278892715772,0.5700946601892775,0.5907801405339781,0.04606751035449885,0.02774155341625371,22349.13952253535,41.43902270876451,11.0,0,1,3,47,1482.5,15.4,26.2,0.7408645276292335,0.11982717117082757
+CAT (tuned),20021.3498029566,0.45918073721700764,4130.291158755297,0.13269281663977184,0.5972104481929726,0.590874976186108,0.08123456288719788,0.05345084723509436,58497.64726501732,8.058537488270943,12.92156862745098,0.21123,7127.110804247856,0.16668947537740073,1658.412974283854,0.08101450844552308,0.601029599608834,0.6338898362509008,0.05045951030687079,0.030933936753504664,22349.13952253535,5.582269457200397,12.0,1,3,2,45,1468.7,19.0,22.9,0.7290552584670231,0.14372608077734617
+TABM (tuned),36462.58337058756,0.9691277276976176,5017.258422060137,0.3543499279245272,0.5996801665884344,0.5894392156662352,0.08005397792131351,0.07918093685262247,80230.4785975727,13.69951936973594,13.696078431372548,0.21042,8420.993191123009,0.2791590425703261,3285.8688373170553,0.1728818165133111,0.5745991505979262,0.592779150360785,0.041022605221466724,0.03536573040794217,47614.825535817596,11.564361688735561,13.5,1,3,2,45,1450.3,20.2,25.4,0.7114527629233511,0.13780161432051177
+GBM (tuned),3088.2834950947295,3.280151636356362,771.2925090199282,0.7008989122586311,0.6529668965012456,0.6341023639146358,0.09282220934736846,0.06957232946152313,11617.562781863631,67.66975356224859,13.911764705882353,0.21181,1667.667911251386,0.5984517203436958,416.9832926671224,0.33384935590955944,0.6262832446301,0.6519687249021858,0.05048061869912612,0.03622173092974084,9501.568897853978,17.561755180687065,12.0,0,0,0,51,1447.2,21.0,27.9,0.7065508021390374,0.08565457797402178
+XGB (tuned + ensemble),6066.175009788823,8.000445660682546,1229.0991373608288,3.120951906510457,0.6509288335184475,0.6437619836489613,0.09352816952933034,0.06895163935820021,14800.692905333448,192.56405868832437,14.196078431372548,0.21458,2256.9276883072325,3.0598979949951173,693.4907982506384,1.6904262577061315,0.6793464009669915,0.6595270919953716,0.05552089944600691,0.03200108278665274,11769.825378159274,82.2877578838671,13.0,0,1,1,49,1439.4,18.9,23.0,0.7000891265597148,0.1013601976229367
+MNCA (tuned + ensemble),50831.51192726024,408.7024568246081,6035.4844365627405,41.810485127597225,0.568211153208397,0.5501368681201666,0.08712345362398452,0.06897277115037478,107380.48324313454,2632.7754273052897,14.490196078431373,0.21294,14486.050127214856,13.240725604693095,4621.665633563503,8.148513113458952,0.5565369335340675,0.528476133918774,0.03527643869682828,0.031199094919591976,89084.88026764008,537.8274695033359,11.0,3,3,5,40,1433.6,23.1,27.3,0.6934046345811051,0.19036002244261158
+CAT (default),190.03885268302784,0.2709793741147243,87.82759080296049,0.13562630112555335,0.643860524899134,0.6473213604262623,0.09253129668253035,0.060816531102421634,428.26912869207973,7.186903437353133,14.813725490196079,0.21142,28.268621895048355,0.1854714552561442,6.827020885706225,0.08026752106160412,0.6746502933037238,0.6439696146756235,0.04582475392187091,0.02917957826918542,113.47556087857672,6.139565341592893,16.0,1,3,1,46,1426.6,25.5,20.1,0.6860516934046346,0.12620896189325026
+TABPFNV2 (tuned + ensemble),10256.531407853461,95.46951768190513,2958.800295487964,47.682041415407696,0.5336461270994103,0.5756859433884367,0.08515145920011706,0.08426450292085469,64069.99714449898,3721.414760799794,15.392156862745098,0.22709,4578.3020022392275,13.798940539360046,3030.145221648074,21.443889733771147,0.6071274732571645,0.6069317461979158,0.03866296841083827,0.03250100481138207,29840.56658077224,861.272449686595,11.0,11,6,1,33,1412.7,26.3,25.9,0.6729055258467023,0.3278729045790513
+XGB (tuned),6066.175009788823,1.6190793797341305,1229.0991373608288,0.7004875821871327,0.7007828758839686,0.6803135064515848,0.09696298847871114,0.07331091982279783,14800.692905333448,40.17091923504828,15.852941176470589,0.21499,2256.9276883072325,0.41774741808573407,693.4907982506384,0.3083513292273418,0.7223432295573234,0.7129456514258546,0.06100086263844462,0.03462028858330468,11769.825378159274,12.136706817415504,14.0,0,0,0,51,1403.4,20.3,22.4,0.6624331550802139,0.07586490654061125
+MNCA (tuned),50831.51192726024,15.584971993005873,6035.4844365627405,1.7226505575752833,0.7032607657750378,0.6433451252379919,0.09326988821561474,0.07237812699834675,107380.48324313454,104.77227719302421,16.029411764705884,0.21328,14486.050127214856,0.585451708899604,4621.665633563503,0.4747242314576746,0.7724401098237351,0.635037134317342,0.06113391902932641,0.0493534926522164,89084.88026764008,26.6476237825924,16.0,1,2,1,47,1401.0,17.2,20.6,0.6584224598930482,0.11520033492162958
+TABICL (default),82.80334805787777,14.446106202929627,7.4655941033985815,1.6847655412525513,0.6267118101348285,0.6591483802547826,0.09205010388137871,0.09066559220446027,132.83095048510535,173.47404322746496,16.637254901960784,0.21659,20.0619904200236,1.316457470258077,6.625686376434735,1.479660415649414,0.7253160785960966,0.7300188790588114,0.05183434289459943,0.0307061823959099,110.37274820175764,91.25701004379168,14.0,6,4,1,40,1386.5,24.7,22.2,0.6446078431372549,0.22069365279362593
+TABM (default),139.62633961878052,1.0737770921524312,22.4928837749369,0.40818286049864,0.7249223512608948,0.7271977899580744,0.10904674351892713,0.10206769049189926,289.28104127940037,12.833742622244758,18.57843137254902,0.21188,40.332407061258955,0.18133597903781468,10.492230631952996,0.13221126395693922,0.8325407781946508,0.7894820610150435,0.05780353607735633,0.03944837820705307,186.77830789084513,10.724711035853641,18.0,0,0,0,51,1345.1,20.4,17.8,0.6004901960784313,0.07809522496642297
+TABPFNV2 (tuned),10256.531407853461,3.508518432739773,2958.800295487964,1.6633483351042642,0.6455033122665624,0.6621142339433628,0.10613855092878187,0.10151317103696923,64069.99714449898,134.3876978041907,18.58823529411765,0.22751,4578.3020022392275,0.5097733656565349,3030.145221648074,0.46198440414964803,0.7572752170216083,0.7071311634998629,0.07667977096051137,0.04654872982993791,29840.56658077224,22.48627830890164,15.5,1,10,1,39,1346.5,26.8,24.7,0.6002673796791443,0.17841696402639748
+REALMLP (tuned),73311.83245680422,0.765440520637695,6156.508467345906,0.43160487228962463,0.7615648419724279,0.710732530428881,0.10375821258342363,0.0827549915204941,177178.04293492812,13.697358431538635,18.607843137254903,0.215,23874.98158947627,0.26132775147755943,6044.758795122033,0.18050895994575464,0.8260416027840659,0.7411687898250225,0.06682574439935507,0.04931132619439678,128255.5951822475,11.19862181751071,17.0,0,0,0,51,1347.2,23.5,22.5,0.5998217468805704,0.06819290912989692
+NN_TORCH (tuned + ensemble),29590.123960411,13.183612702147375,3729.842332819238,3.5618312259496654,0.7913423906704912,0.7660666706649322,0.10611131869401512,0.09038128659154394,97942.50427646744,194.194370742189,19.54901960784314,0.21479,10848.660208092795,3.946354971991645,2874.6743506773596,1.9516254299583915,0.9196144626274988,0.8406520609695045,0.06624137678637454,0.05785446391646678,61441.834728019065,144.9992483814283,20.0,0,0,0,51,1327.2,24.7,21.6,0.5784313725490197,0.06731049079573719
+TABPFNV2 (default),12.045264010803372,0.825882379421741,4.478694030451919,0.4530850283232118,0.6822105535450186,0.7166511738002558,0.11639838627727908,0.1129506889897436,69.53545044274777,29.75030650326298,20.029411764705884,0.22887,9.117408725950453,0.42119165261586505,3.3572004182270923,0.3130195506083406,1.0,0.8502991289200135,0.07592598037026033,0.05279556334085833,52.331266670190345,18.512009051191257,22.0,4,1,6,40,1319.1,21.2,21.6,0.5675133689839572,0.18240480693759595
+MNCA (default),252.05720278889527,8.333707038145958,17.796029105655933,1.1526772129094414,0.8466123430768121,0.7953614233947742,0.1283337233918568,0.09667613065132963,329.3551130182264,62.49957238602975,20.96078431372549,0.21885,36.0034454398685,0.5316168732113309,14.869495839530392,0.30768591310277943,1.0,0.8801463886825595,0.07424254289688792,0.06351531911154724,236.99495784925895,20.43419277465042,22.0,1,0,0,50,1298.0,25.2,21.5,0.5463458110516934,0.07959304417991664
+TABDPT (default),166.18249968905855,63.11337411850366,27.980557312145997,23.24231264879125,0.7067604944091354,0.699237694368164,0.12276620862052218,0.09536848200243395,577.6877537613715,1486.1376930964866,20.980392156862745,0.22562,99.10453534126282,28.39870807859633,22.52690614988171,8.550738306685618,1.0,0.8691048403402053,0.05029899916003,0.0433680924463882,528.528670151851,1255.434427440434,22.0,7,0,3,41,1299.0,24.0,27.3,0.5459001782531194,0.20400425047519355
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diff --git a/data/full-reg/leaderboard.tex b/data/full-reg/leaderboard.tex
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+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+AutoGluon 1.3 (4h) & \textcolor{gold}{\textbf{1887${}_{-76,+77}$}} & \textcolor{gold}{\textbf{0.735}} & \textcolor{gold}{\textbf{3.7}} & \textcolor{silver}{\textbf{2.3}} & \textcolor{bronze}{\textbf{2}} & \textcolor{silver}{\textbf{2.3\%}} & 1625.74 & 6.76 \\
+RealMLP (T+E) & \textcolor{silver}{\textbf{1818${}_{-57,+65}$}} & \textcolor{silver}{\textbf{0.734}} & \textcolor{silver}{\textbf{4.8}} & \textcolor{bronze}{\textbf{2.7}} & \textcolor{bronze}{\textbf{2}} & \textcolor{gold}{\textbf{1.6\%}} & 6567.16 & 2.48 \\
+ModernNCA (T+E) & \textcolor{bronze}{\textbf{1701${}_{-72,+54}$}} & 0.603 & \textcolor{bronze}{\textbf{7.3}} & \textcolor{bronze}{\textbf{2.7}} & \textcolor{silver}{\textbf{3}} & 3.4\% & 3779.52 & 7.69 \\
+CatBoost (T+E) & 1668${}_{-56,+71}$ & 0.536 & 8.1 & 5.8 & 0 & 4.1\% & 3552.96 & 0.97 \\
+LightGBM (T+E) & 1660${}_{-65,+60}$ & 0.531 & 8.2 & 6.1 & 0 & 4.6\% & 700.15 & 9.32 \\
+TabDPT (D) & 1659${}_{-52,+60}$ & \textcolor{bronze}{\textbf{0.635}} & 8.2 & \textcolor{gold}{\textbf{2.2}} & \textcolor{gold}{\textbf{5}} & \textcolor{bronze}{\textbf{2.5\%}} & 22.53 & 8.55 \\
+CatBoost (T) & 1625${}_{-55,+40}$ & 0.512 & 9.3 & 4.9 & 1 & 4.2\% & 3552.96 & 0.10 \\
+TabM (T+E) & 1623${}_{-71,+64}$ & 0.488 & 9.5 & 5.2 & 0 & 2.8\% & 4158.29 & 1.41 \\
+LightGBM (T) & 1538${}_{-47,+51}$ & 0.432 & 11.8 & 10.3 & 0 & 5.2\% & 700.15 & 0.97 \\
+XGBoost (T+E) & 1535${}_{-47,+58}$ & 0.432 & 11.8 & 11.1 & 0 & 5.1\% & 834.93 & 2.61 \\
+XGBoost (T) & 1514${}_{-52,+47}$ & 0.398 & 12.6 & 12.1 & 0 & 5.3\% & 834.93 & 0.39 \\
+ModernNCA (T) & 1484${}_{-41,+71}$ & 0.351 & 13.5 & 6.5 & 0 & 5.5\% & 3779.52 & 0.40 \\
+TabM (T) & 1478${}_{-74,+58}$ & 0.380 & 13.9 & 10.3 & 0 & 3.9\% & 4158.29 & 0.17 \\
+CatBoost (D) & 1465${}_{-45,+61}$ & 0.389 & 14.0 & 10.2 & 0 & 5.9\% & 10.89 & 0.09 \\
+RealMLP (T) & 1436${}_{-49,+44}$ & 0.344 & 15.2 & 10.9 & 0 & 4.2\% & 6567.16 & 0.09 \\
+ModernNCA (D) & 1367${}_{-54,+55}$ & 0.209 & 17.5 & 13.0 & 0 & 7.1\% & 15.50 & 0.30 \\
+TabM (D) & 1347${}_{-54,+60}$ & 0.287 & 18.0 & 14.3 & 0 & 5.6\% & 13.32 & 0.13 \\
+TorchMLP (T+E) & 1317${}_{-48,+58}$ & 0.171 & 19.2 & 14.1 & 0 & 7.2\% & 4608.59 & 1.23 \\
+RealMLP (D) & 1297${}_{-53,+57}$ & 0.137 & 19.6 & 15.5 & 0 & 6.7\% & 33.36 & 0.08 \\
+ExtraTrees (T+E) & 1287${}_{-58,+51}$ & 0.157 & 20.2 & 11.8 & 0 & 9.7\% & 158.22 & 0.84 \\
+LightGBM (D) & 1268${}_{-65,+57}$ & 0.070 & 20.8 & 20.3 & 0 & 7.7\% & 2.11 & 0.27 \\
+ExtraTrees (T) & 1262${}_{-52,+55}$ & 0.129 & 21.0 & 14.5 & 0 & 9.9\% & 158.22 & 0.15 \\
+TorchMLP (T) & 1251${}_{-66,+43}$ & 0.123 & 21.5 & 18.8 & 0 & 8.1\% & 4608.59 & 0.10 \\
+XGBoost (D) & 1220${}_{-50,+45}$ & 0.111 & 22.6 & 20.4 & 0 & 8.4\% & 2.24 & 0.24 \\
+RandomForest (T+E) & 1211${}_{-63,+44}$ & 0.066 & 23.2 & 20.4 & 0 & 10.6\% & 515.73 & 0.77 \\
+RandomForest (T) & 1150${}_{-54,+52}$ & 0.048 & 24.8 & 22.7 & 0 & 11.1\% & 515.73 & 0.12 \\
+EBM (T+E) & 1145${}_{-51,+63}$ & 0.160 & 24.8 & 12.9 & 0 & 13.4\% & 1890.68 & 0.13 \\
+ExtraTrees (D) & 1114${}_{-58,+45}$ & 0.061 & 26.0 & 22.8 & 0 & 11.9\% & 0.47 & 0.06 \\
+EBM (T) & 1104${}_{-57,+55}$ & 0.137 & 26.4 & 16.4 & 0 & 13.9\% & 1890.68 & 0.01 \\
+FastaiMLP (T+E) & 1067${}_{-52,+51}$ & 0.023 & 27.4 & 25.8 & 0 & 11.9\% & 540.06 & 2.67 \\
+TorchMLP (D) & 1048${}_{-72,+52}$ & 0.016 & 28.1 & 26.3 & 0 & 11.6\% & 20.48 & 0.08 \\
+EBM (D) & 1025${}_{-48,+60}$ & 0.093 & 28.5 & 26.0 & 0 & 14.8\% & 6.33 & 0.04 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.000 & 29.2 & 28.6 & 0 & 12.6\% & 0.53 & 0.06 \\
+FastaiMLP (T) & 994${}_{-54,+57}$ & 0.013 & 29.2 & 28.2 & 0 & 12.4\% & 540.06 & 0.32 \\
+FastaiMLP (D) & 858${}_{-56,+52}$ & 0.000 & 32.2 & 31.8 & 0 & 16.9\% & 2.60 & 0.39 \\
+KNN (T+E) & 507${}_{-111,+65}$ & 0.000 & 37.0 & 36.8 & 0 & 36.0\% & 2.43 & 0.14 \\
+Linear (T+E) & 470${}_{-96,+65}$ & 0.000 & 37.3 & 37.2 & 0 & 35.3\% & 45.74 & 0.11 \\
+KNN (T) & 413${}_{-101,+81}$ & 0.000 & 38.0 & 37.8 & 0 & 36.6\% & 2.43 & 0.03 \\
+Linear (T) & 398${}_{-99,+88}$ & 0.000 & 37.9 & 37.8 & 0 & 35.5\% & 45.74 & 0.05 \\
+Linear (D) & 276${}_{-99,+71}$ & 0.000 & 39.2 & 39.1 & 0 & 38.0\% & 1.19 & 0.09 \\
+KNN (D) & 200${}_{-136,+122}$ & 0.000 & 39.7 & 39.6 & 0 & 40.6\% & 0.04 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/full-reg/tabarena_leaderboard.csv b/data/full-reg/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9ea4f6b7c6ac5a4ab8b4f730d55e3b314338f980
--- /dev/null
+++ b/data/full-reg/tabarena_leaderboard.csv
@@ -0,0 +1,42 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+AutoGluon 1.3 (4h),10053.09853225386,65.70725388180496,3138.716102354025,8.201870690838728,0.26461131829744206,0.27698219418045594,0.02291895685939331,0.035964926580766866,79227.35072832079,1344.7158520024777,3.6923076923076925,4.16484,12938.261539538702,10.17258334159851,1625.738447135909,6.759745919437983,0.23133371369265834,0.31069761727157824,0.015074633964319739,0.016643068102528722,85166.92529682687,517.5224332581762,3.0,2,3,4,4,1887.4,76.5,75.1,0.9326923076923077,0.4262820512820513
+REALMLP (tuned + ensemble),67709.67308180964,12.011677133731354,6963.87401913588,6.973854948645224,0.26596078198184286,0.29443527620235754,0.015584141131814132,0.027230868655267488,303258.6729720159,231.34066267352048,4.846153846153846,4.12234,28517.58209461636,4.232689115736219,6567.164030631503,2.480181700638205,0.19393332805279354,0.29742106435254645,0.008809518245537662,0.018856294653559095,203710.74184331874,227.32654052633927,4.0,2,2,2,7,1817.9,64.3,56.7,0.9038461538461539,0.3723776223776224
+MNCA (tuned + ensemble),31759.101002019288,48.99773550746787,6166.048475961011,17.643273328787647,0.39661026996847265,0.3574009968929841,0.034393187333226535,0.0356806857944739,186080.1526868885,677.6872983460363,7.3076923076923075,4.48697,16310.556293937894,10.227302259869045,3779.5248398651206,7.690422738079533,0.35096562881515253,0.35660094620644167,0.019057076597787104,0.023956472897060137,198038.2195884601,507.5335424076248,5.0,3,1,1,8,1701.4,53.3,71.3,0.8423076923076923,0.371127337574706
+CAT (tuned + ensemble),30302.612278386874,4.803950041991014,6846.603031359736,1.23549623448159,0.46394270088709505,0.4802827806871086,0.04080599575171776,0.05421914583673029,150000.99966111325,116.73562230823504,8.076923076923077,4.20863,22090.5574801498,2.685193909539117,3552.958864906998,0.9657383741190037,0.4150140849407447,0.4814904032190787,0.020503839297461468,0.036001021424025365,164350.82166574517,80.04598787510639,9.0,0,1,1,11,1668.1,70.5,55.8,0.823076923076923,0.1736409564344787
+GBM (tuned + ensemble),3469.6354441180188,51.50608464469258,806.792675338677,8.57889161766087,0.4687885136784251,0.4779542732223701,0.0459142163370746,0.05847756014580863,20529.34803212188,1135.938430886033,8.153846153846153,4.21165,3055.419878217909,17.84196005927192,700.1537746143185,9.321818212785747,0.41525976638432377,0.45795538731815566,0.021205168859483203,0.03428335611022812,21786.570035668443,556.5446710812895,6.0,0,0,1,12,1660.1,60.0,64.2,0.8211538461538461,0.16348235098235098
+TABDPT (default),150.02111775732448,54.38835432835114,28.728808967170515,25.042434382939593,0.36524485788247774,0.3757566695219451,0.0248363952945231,0.03287367328135333,859.9949957462201,1916.4746059233426,8.23076923076923,4.26747,137.4807067182329,29.239719518025716,22.52690614988171,8.547073882575031,0.28825222902504594,0.3825474634976233,0.024988993973347418,0.018753299523578005,904.9754515353668,1821.0928942426492,6.0,5,0,0,8,1659.1,59.1,52.0,0.8192307692307692,0.4508083796545335
+CAT (tuned),30302.612278386874,0.5550543483505901,6846.603031359736,0.16909210735792113,0.48768370669415817,0.49734395231843176,0.04200103064707736,0.05784545109220123,150000.99966111325,13.40589862804556,9.26923076923077,4.23064,22090.5574801498,0.3789627022213406,3552.958864906998,0.10450043094654878,0.5120020111687084,0.49257184784034574,0.02559764776763218,0.03741223775007456,164350.82166574517,10.277501137463553,8.0,1,1,0,11,1624.9,39.7,54.9,0.7932692307692307,0.20512820512820512
+TABM (tuned + ensemble),42663.03407314721,6.401903104782104,7337.2216133773,2.4450322937588758,0.5116630985629478,0.49582652586594606,0.028484933382026085,0.07757253674263247,180683.6213219068,120.23686774247648,9.538461538461538,4.1458,20820.912871148852,3.0152715841929116,4158.291053548661,1.4096720886484275,0.43090407292838107,0.45442246548589066,0.02530931453787877,0.01972389522417027,166291.5239595113,107.26839105814781,6.0,0,2,0,11,1623.4,63.2,70.8,0.7865384615384615,0.19279400424137266
+XGB (tuned + ensemble),6384.208144447945,12.513759183476113,1409.0815132938305,4.086239880001324,0.567688570586379,0.5777747629771025,0.05130276883821868,0.06605947558091682,26398.72664659111,353.2622484982949,11.76923076923077,4.22219,2596.930354913076,6.618133616447449,834.9300717202715,2.614265349176195,0.6256631137377601,0.6286907892907813,0.027928072769987877,0.04797658607386891,26370.57574541831,164.97865636299682,11.0,0,0,0,13,1534.8,57.5,46.7,0.7307692307692307,0.08990147610057113
+GBM (tuned),3469.6354441180188,7.385669826034806,806.792675338677,1.273710690539978,0.5677497712335507,0.5662287079782077,0.0520873055824581,0.07113770565213798,20529.34803212188,170.85098129275468,11.846153846153847,4.23482,3055.419878217909,2.690451833936903,700.1537746143185,0.9682498776019389,0.5552965190223861,0.5777514545736621,0.027972044011269404,0.03922298758674819,21786.570035668443,130.77892053638826,12.0,0,0,0,13,1538.0,50.6,46.2,0.7288461538461538,0.09731628351537853
+XGB (tuned),6384.208144447945,2.5463058025409016,1409.0815132938305,0.7958717570786976,0.6023900941776976,0.6057381247672692,0.052778191073056716,0.07014560263282078,26398.72664659111,74.35901041305158,12.576923076923077,4.23559,2596.930354913076,1.7797584003872342,834.9300717202715,0.3883258596259137,0.6620816436761664,0.6405605013277449,0.02927659597280985,0.04927060675426248,26370.57574541831,37.05707269936294,12.0,0,0,0,13,1514.0,46.4,51.4,0.7105769230769231,0.08262651752470757
+MNCA (tuned),31759.101002019288,2.180037997319148,6166.048475961011,0.7699041848491119,0.6494157477338965,0.5952759886308904,0.05539455073257844,0.07068306551551398,186080.1526868885,32.66971509741366,13.461538461538462,4.74895,16310.556293937894,0.48738079600863987,3779.5248398651206,0.3958729871859153,0.686862000079896,0.5700170149041335,0.027829209062692706,0.0493534926522164,198038.2195884601,22.639832589759934,14.0,0,2,1,10,1484.4,70.4,40.2,0.6884615384615385,0.1533190381368519
+TABM (tuned),42663.03407314721,0.6790118826760185,7337.2216133773,0.2826346699151617,0.6195303355392976,0.5994402285038597,0.03858566517030648,0.09844026405388778,180683.6213219068,13.142332198971314,13.923076923076923,4.26932,20820.912871148852,0.3543446593814426,4158.291053548661,0.16724776809008424,0.5658117450288995,0.5832600605513046,0.034427431733584535,0.027702057966854137,166291.5239595113,12.524825096296247,11.0,0,0,0,13,1478.2,57.9,73.1,0.676923076923077,0.09674719867027558
+CAT (default),101.69209800418625,0.27084102752881173,23.09400681118272,0.13269262448628016,0.6107641551712003,0.6248418005606562,0.05895124482581516,0.07549119565100736,496.3567653671193,9.26273127760114,13.961538461538462,4.21395,62.699359814325966,0.2736650307973226,10.889876924735699,0.09199146861016888,0.5847419575275037,0.6538434834236965,0.021738610101570965,0.03712451320809096,412.72250349454265,8.72239577968297,13.0,0,0,0,13,1464.8,60.5,45.0,0.6759615384615385,0.09783139765925522
+REALMLP (tuned),67709.67308180964,0.5510838443397457,6963.87401913588,0.35676328285131836,0.655870427194709,0.6192525352803894,0.041781108455330826,0.0794078602157222,303258.6729720159,12.866326813784351,15.23076923076923,4.2931,28517.58209461636,0.2134650813208686,6567.164030631503,0.09160214336768219,0.67949922992921,0.6789002713578278,0.0397756810178872,0.04931132619439678,203710.74184331874,10.089309852682138,16.0,0,0,1,12,1435.5,43.8,48.5,0.6442307692307693,0.09133017251832487
+MNCA (default),99.58075828980176,2.0463109099966847,18.34482305470451,0.7039566701063034,0.7905734390801098,0.758807785538453,0.07077992898843004,0.09644209551436314,547.7971597202185,27.012858819060174,17.53846153846154,4.94033,49.107323222690155,0.41290783882141113,15.50085128260229,0.29868905742963153,0.9481480616823943,0.9086181882493612,0.03934956674595991,0.09386686583837221,536.5510136544884,15.897990578305649,18.0,0,0,0,13,1367.3,54.5,53.7,0.5865384615384616,0.07718910857215008
+TABM (default),109.08048485323914,0.5565733836247371,30.11061011517182,0.2424694191265854,0.7132806572850755,0.7147282609076164,0.05630269517815623,0.12970614775630301,580.6229196238922,9.300928058329742,18.0,4.27071,65.0270922978719,0.1736939483218723,13.315938751381204,0.13061717308512005,0.6984407490367052,0.6980606107077207,0.05131334419415079,0.051190784688604524,354.8698900002396,7.016702417594013,16.0,0,0,0,13,1347.3,59.8,53.4,0.575,0.06992665938463788
+NN_TORCH (tuned + ensemble),44960.17932087947,4.03415755573501,5714.412959426929,1.3298489659738517,0.8294336223884518,0.8131262211817997,0.07233788099307822,0.11855200253632468,220430.49479235077,97.5953017172201,19.153846153846153,4.65351,15497.21247045199,2.8239229255252414,4608.594420268999,1.2325370779691025,0.9419042422995463,0.8828910186177211,0.05961664534372901,0.0834039942118561,177697.9133637009,96.53385472639289,20.0,0,0,0,13,1316.9,57.9,47.4,0.5461538461538461,0.0711002949006045
+REALMLP (default),360.5571643448284,0.5610987406510572,36.16407680854995,0.35279691100634736,0.8625329178435776,0.8317168418227403,0.06669325379717181,0.12718726997120225,1586.4859687066598,12.051410462198454,19.615384615384617,4.76491,145.05578433142767,0.20847519238789877,33.35619963926919,0.08008880801221668,0.9645369038929024,0.9167568200448138,0.0482796143974169,0.09560361463677074,955.4745851239355,8.821199701887194,20.0,0,0,0,13,1297.2,56.7,52.7,0.5346153846153846,0.06456321227379722
+XT (tuned + ensemble),1206.5194327423715,3.367576224375994,442.36567464742467,1.069316176291616,0.8432098887288747,0.8539657200625945,0.09727743600772741,0.11929865478792087,8220.916376158188,88.72398178178156,20.23076923076923,5.04794,766.4287914435068,3.576531834072537,158.22496863160976,0.8436571643025992,1.0,1.0,0.049883734447319306,0.11775243552615773,5379.1926370125775,93.77443700336585,24.0,0,0,1,12,1287.1,50.1,57.9,0.5192307692307693,0.08465359052403586
+GBM (default),10.696644908750159,2.245801542559241,3.3219193518156818,0.4939224454467883,0.9303753212721771,0.8861460110602865,0.07714163256645466,0.11821573250771258,80.53396018410795,58.38494586351267,20.76923076923077,4.4838,7.6057972113291425,0.9335102770063612,2.1107352135741744,0.2745991643955073,0.9919628610131755,0.9232692312204189,0.04937573951369445,0.09720745168389198,91.35379089849596,24.071571166022103,20.0,0,0,0,13,1267.6,57.0,64.6,0.5057692307692307,0.0493082737247497
+XT (tuned),1206.5194327423715,0.45855456478575357,442.36567464742467,0.18106308268317445,0.8708387105454042,0.8747725588350008,0.09945068180602606,0.12636366597608262,8220.916376158188,14.733646953552363,21.03846153846154,5.07281,766.4287914435068,0.34996385044521755,158.22496863160976,0.15116311924152026,1.0,0.9988849454089667,0.050434912392149145,0.1220745399591585,5379.1926370125775,14.85253877204164,23.0,0,0,0,13,1261.7,54.5,51.3,0.49903846153846154,0.0689176100929359
+NN_TORCH (tuned),44960.17932087947,0.2904610134597517,5714.412959426929,0.10578639607158029,0.8767833734464541,0.8672270215081037,0.0805298918130512,0.13726924760597434,220430.49479235077,7.008617614539204,21.53846153846154,4.7148,15497.21247045199,0.18749599986606175,4608.594420268999,0.09690652350320204,1.0,0.9256758910554999,0.0730528979706524,0.0987961550603162,177697.9133637009,6.6162587674624795,21.0,0,0,0,13,1250.6,42.6,65.5,0.48653846153846153,0.05309187244027516
+XGB (default),12.109628748486186,0.9366908014330091,3.1242101566662273,0.3058622971724495,0.8890754852284094,0.8651216835463518,0.08407275864462525,0.12418615325132822,67.76669386312491,28.981192639888032,22.576923076923077,4.75458,7.79438853263855,0.5301833947499593,2.2441525977447814,0.24247013095584213,1.0,0.9462051303684406,0.053268364359151055,0.09620970472363667,75.78126190262087,16.70456778360137,23.0,0,0,0,13,1220.0,44.6,49.8,0.46057692307692305,0.04904036202859359
+RF (tuned + ensemble),2056.9684221919783,3.432567118171953,500.6828145780351,1.0841887922659657,0.9341058521704168,0.9236706018825189,0.105513096504343,0.12898261780451312,10863.776799780928,90.24173062908258,23.153846153846153,5.10626,1088.11842862765,1.444333102968004,515.7302180242054,0.7709478321252661,1.0,1.0,0.06747831844148078,0.12984639499235218,11909.387526812274,91.13733276245972,25.0,0,0,0,13,1210.7,43.7,62.2,0.4461538461538462,0.04896049394550144
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+LR (default),4.903454369968838,0.19863318235446245,2.033074367074023,0.07563726553282422,1.0,1.0,0.3796067152688096,0.7701691922754811,45.803264898726965,4.508173919929611,39.15384615384615,8.22758,5.286295996771918,0.09712121221754286,1.191311693685635,0.0859815087197251,1.0,1.0,0.38694683448707623,0.8770019722050186,34.79737790723628,4.100500531123858,39.0,0,0,0,13,275.5,70.5,98.7,0.046153846153846156,0.02558384584157271
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diff --git a/data/full/leaderboard.tex b/data/full/leaderboard.tex
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+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+AutoGluon 1.3 (4h) & \textcolor{gold}{\textbf{1644${}_{-30,+39}$}} & \textcolor{gold}{\textbf{0.608}} & \textcolor{gold}{\textbf{6.8}} & \textcolor{gold}{\textbf{2.4}} & \textcolor{gold}{\textbf{13}} & \textcolor{gold}{\textbf{4.0\%}} & 1408.78 & 3.34 \\
+RealMLP (T+E) & \textcolor{silver}{\textbf{1600${}_{-33,+24}$}} & \textcolor{silver}{\textbf{0.499}} & \textcolor{silver}{\textbf{8.1}} & 4.2 & 3 & \textcolor{bronze}{\textbf{5.7\%}} & 6044.76 & 3.80 \\
+TabM (T+E) & \textcolor{bronze}{\textbf{1594${}_{-36,+28}$}} & \textcolor{bronze}{\textbf{0.491}} & \textcolor{bronze}{\textbf{8.3}} & \textcolor{silver}{\textbf{3.4}} & \textcolor{bronze}{\textbf{6}} & \textcolor{silver}{\textbf{5.0\%}} & 3285.87 & 1.47 \\
+LightGBM (T+E) & 1573${}_{-26,+30}$ & 0.437 & 8.9 & 5.6 & 1 & 6.9\% & 416.98 & 2.64 \\
+CatBoost (T+E) & 1521${}_{-25,+35}$ & 0.419 & 10.6 & 6.6 & 1 & 6.0\% & 1658.41 & 0.65 \\
+CatBoost (T) & 1506${}_{-27,+28}$ & 0.403 & 11.1 & 5.8 & 2 & 6.2\% & 1658.41 & 0.08 \\
+TabM (T) & 1489${}_{-30,+29}$ & 0.400 & 11.8 & 5.9 & 1 & 6.1\% & 3285.87 & 0.17 \\
+LightGBM (T) & 1483${}_{-24,+25}$ & 0.347 & 12.0 & 10.2 & 0 & 7.6\% & 416.98 & 0.33 \\
+XGBoost (T+E) & 1472${}_{-29,+30}$ & 0.349 & 12.3 & 8.6 & 0 & 7.7\% & 693.49 & 1.69 \\
+ModernNCA (T+E) & 1468${}_{-33,+30}$ & 0.432 & 12.6 & 4.2 & 5 & 6.9\% & 4621.67 & 8.15 \\
+CatBoost (D) & 1459${}_{-28,+31}$ & 0.356 & 12.9 & 6.6 & 2 & 7.6\% & 6.83 & 0.08 \\
+XGBoost (T) & 1435${}_{-31,+27}$ & 0.299 & 13.9 & 11.7 & 0 & 8.0\% & 693.49 & 0.31 \\
+ModernNCA (T) & 1429${}_{-20,+29}$ & 0.297 & 14.0 & 6.7 & 2 & 7.6\% & 4621.67 & 0.47 \\
+TabM (D) & 1373${}_{-29,+31}$ & 0.275 & 16.5 & 10.7 & 0 & 9.3\% & 10.49 & 0.13 \\
+RealMLP (T) & 1373${}_{-29,+30}$ & 0.238 & 16.4 & 11.9 & 0 & 8.7\% & 6044.76 & 0.18 \\
+TorchMLP (T+E) & 1352${}_{-29,+23}$ & 0.209 & 17.3 & 11.7 & 0 & 9.0\% & 2874.67 & 1.95 \\
+ModernNCA (D) & 1319${}_{-23,+25}$ & 0.153 & 18.7 & 10.7 & 1 & 11.2\% & 14.87 & 0.31 \\
+TabDPT (D) & 1318${}_{-26,+34}$ & 0.293 & 18.8 & \textcolor{bronze}{\textbf{4.1}} & \textcolor{silver}{\textbf{9}} & 10.8\% & 22.53 & 8.55 \\
+EBM (T+E) & 1315${}_{-23,+24}$ & 0.175 & 18.9 & 10.8 & 0 & 12.9\% & 1331.68 & 0.20 \\
+FastaiMLP (T+E) & 1270${}_{-29,+30}$ & 0.152 & 20.8 & 12.4 & 0 & 12.8\% & 593.24 & 4.47 \\
+ExtraTrees (T+E) & 1257${}_{-29,+29}$ & 0.117 & 21.4 & 12.8 & 0 & 13.0\% & 183.02 & 0.76 \\
+EBM (T) & 1256${}_{-26,+24}$ & 0.126 & 21.4 & 14.9 & 0 & 13.6\% & 1331.68 & 0.02 \\
+RealMLP (D) & 1249${}_{-26,+27}$ & 0.102 & 21.7 & 17.6 & 0 & 11.1\% & 35.36 & 0.19 \\
+XGBoost (D) & 1247${}_{-31,+25}$ & 0.106 & 21.8 & 17.4 & 0 & 11.1\% & 1.94 & 0.12 \\
+TorchMLP (T) & 1240${}_{-25,+30}$ & 0.103 & 22.0 & 18.2 & 0 & 11.2\% & 2874.67 & 0.13 \\
+EBM (D) & 1220${}_{-27,+26}$ & 0.125 & 23.0 & 9.5 & 3 & 14.6\% & 4.67 & 0.04 \\
+LightGBM (D) & 1213${}_{-26,+27}$ & 0.074 & 23.2 & 20.9 & 0 & 12.1\% & 1.96 & 0.14 \\
+ExtraTrees (T) & 1210${}_{-28,+28}$ & 0.092 & 23.3 & 14.8 & 0 & 14.1\% & 183.02 & 0.09 \\
+RandomForest (T+E) & 1208${}_{-25,+25}$ & 0.096 & 23.4 & 14.0 & 0 & 13.6\% & 373.18 & 0.77 \\
+FastaiMLP (T) & 1179${}_{-28,+20}$ & 0.070 & 24.6 & 19.7 & 0 & 14.5\% & 593.24 & 0.31 \\
+RandomForest (T) & 1153${}_{-28,+28}$ & 0.069 & 25.5 & 16.6 & 0 & 14.7\% & 373.18 & 0.09 \\
+TorchMLP (D) & 1070${}_{-25,+23}$ & 0.019 & 28.7 & 26.3 & 0 & 16.2\% & 9.99 & 0.13 \\
+FastaiMLP (D) & 1014${}_{-28,+26}$ & 0.019 & 30.5 & 27.9 & 0 & 19.8\% & 2.86 & 0.37 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.008 & 30.9 & 29.1 & 0 & 20.1\% & 0.43 & 0.05 \\
+ExtraTrees (D) & 977${}_{-29,+23}$ & 0.023 & 31.5 & 27.8 & 0 & 22.0\% & 0.25 & 0.05 \\
+Linear (T+E) & 924${}_{-26,+29}$ & 0.031 & 33.0 & 25.1 & 0 & 29.6\% & 47.49 & 0.17 \\
+Linear (T) & 886${}_{-29,+26}$ & 0.019 & 33.9 & 27.1 & 0 & 30.4\% & 47.49 & 0.07 \\
+Linear (D) & 864${}_{-30,+31}$ & 0.014 & 34.5 & 20.5 & 1 & 32.0\% & 1.52 & 0.09 \\
+KNN (T+E) & 687${}_{-31,+39}$ & 0.000 & 37.5 & 37.2 & 0 & 44.7\% & 3.26 & 0.18 \\
+KNN (T) & 608${}_{-44,+38}$ & 0.000 & 38.5 & 38.4 & 0 & 46.3\% & 3.26 & 0.04 \\
+KNN (D) & 449${}_{-72,+46}$ & 0.000 & 40.0 & 39.8 & 0 & 53.7\% & 0.05 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/full/tabarena_leaderboard.csv b/data/full/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5d3fd1ed6fc3802f6ed16693b2a442b49a8d4405
--- /dev/null
+++ b/data/full/tabarena_leaderboard.csv
@@ -0,0 +1,42 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+AutoGluon 1.3 (4h),8462.98349033711,33.138286938542635,2912.1242917208747,4.391016265244401,0.39210856520445686,0.34904137217357156,0.03975956193049951,0.029706646727682955,43301.56973340033,539.3969074295995,6.784313725490196,0.2055,7367.614226023356,3.9490213659074573,1408.7828331379249,3.337414261487274,0.33725328327072535,0.31069761727157824,0.015883109174547605,0.0137838373861104,33121.993698706065,185.27028732280235,4.0,13,4,8,26,1643.5,38.7,29.1,0.8553921568627451,0.41392781688875935
+REALMLP (tuned + ensemble),73311.83245680422,16.208294235543228,6156.508467345906,8.023682171781228,0.5008360344149994,0.46214916919286664,0.05665914931491045,0.03807847400553225,177178.04293492812,276.8359259771498,8.058823529411764,0.20511,23874.98158947627,5.369593381881714,6044.758795122033,3.804109742244085,0.48215030896347355,0.45450333260642206,0.021990739446628438,0.018856294653559095,128255.5951822475,249.18630751354758,6.0,3,5,6,37,1600.4,23.8,33.0,0.8235294117647058,0.23789224905961384
+TABM (tuned + ensemble),36462.58337058756,8.791583087761158,5017.258422060137,3.1802055454964386,0.5087786520610308,0.47533274202313947,0.04982932238357392,0.054412466910024854,80230.4785975727,130.9039885664859,8.264705882352942,0.20535,8420.993191123009,2.7141049438052707,3285.8688373170553,1.4723486146222118,0.47020083976979304,0.4531573745622899,0.02530931453787877,0.014678650112589856,47614.825535817596,111.53030159160512,6.0,6,9,2,34,1594.0,27.1,35.9,0.8183823529411764,0.29738848927703415
+GBM (tuned + ensemble),3088.2834950947295,22.058380506500956,771.2925090199282,4.080656504886727,0.5633713731527658,0.5348599223259493,0.06897871778751817,0.0503304665001517,11617.562781863631,427.3461655076801,8.882352941176471,0.2112,1667.667911251386,3.7860276963975696,416.9832926671224,2.6387318875879693,0.5860060403960241,0.548121690931643,0.02669651687138408,0.020199925754737696,9501.568897853978,118.5632719261967,7.0,1,3,2,45,1573.0,29.1,25.1,0.8029411764705883,0.1791687775251789
+CAT (tuned + ensemble),20021.3498029566,3.148214934334516,4130.291158755297,0.9792413821552454,0.580901607522528,0.5441016791405096,0.06042198162454047,0.04401299938732732,58497.64726501732,62.60491239485109,10.617647058823529,0.21079,7127.110804247856,1.3727677133348253,1658.412974283854,0.65278892715772,0.5700946601892775,0.5474283264159685,0.02741677546323218,0.025036082174699086,22349.13952253535,41.43902270876451,10.0,1,2,2,46,1521.3,34.8,24.5,0.7595588235294117,0.15165092074534797
+CAT (tuned),20021.3498029566,0.45918073721700764,4130.291158755297,0.13269281663977184,0.5972104481929726,0.5574797218986534,0.0623947409741932,0.044677601016314815,58497.64726501732,8.058537488270943,11.117647058823529,0.21123,7127.110804247856,0.16668947537740073,1658.412974283854,0.08101450844552308,0.601029599608834,0.5872284062129579,0.033769165522777866,0.02645273717109452,22349.13952253535,5.582269457200397,10.0,2,4,2,43,1506.4,27.1,26.7,0.7470588235294118,0.1738821747274908
+TABM (tuned),36462.58337058756,0.9691277276976176,5017.258422060137,0.3543499279245272,0.5996801665884344,0.5469831569788877,0.06058069094813254,0.07033713181811814,80230.4785975727,13.69951936973594,11.77450980392157,0.21042,8420.993191123009,0.2791590425703261,3285.8688373170553,0.1728818165133111,0.5745991505979262,0.5819755629743474,0.03376668052514742,0.02597899958681764,47614.825535817596,11.564361688735561,11.0,1,5,3,42,1488.7,28.9,29.5,0.7306372549019607,0.16832488064973897
+GBM (tuned),3088.2834950947295,3.280151636356362,771.2925090199282,0.7008989122586311,0.6529668965012456,0.6042462614423542,0.0762989584456341,0.060843556700722376,11617.562781863631,67.66975356224859,12.029411764705882,0.21181,1667.667911251386,0.5984517203436958,416.9832926671224,0.33384935590955944,0.6262832446301,0.6276669488795971,0.0328575001678707,0.027189476852237368,9501.568897853978,17.561755180687065,12.0,0,0,0,51,1483.0,24.3,23.7,0.7242647058823529,0.0977555613690435
+XGB (tuned + ensemble),6066.175009788823,8.000445660682546,1229.0991373608288,3.120951906510457,0.6509288335184475,0.6177441419082373,0.07677541704493614,0.060419782876437525,14800.692905333448,192.56405868832437,12.323529411764707,0.21458,2256.9276883072325,3.0598979949951173,693.4907982506384,1.6904262577061315,0.6793464009669915,0.6380210093138985,0.034214063223028224,0.029912916021147633,11769.825378159274,82.2877578838671,11.0,0,2,0,49,1471.8,29.4,28.2,0.7169117647058824,0.11652490624158723
+MNCA (tuned + ensemble),50831.51192726024,408.7024568246081,6035.4844365627405,41.810485127597225,0.568211153208397,0.5149849170519787,0.0688274981267202,0.0605520811121876,107380.48324313454,2632.7754273052897,12.607843137254902,0.21294,14486.050127214856,13.240725604693095,4621.665633563503,8.148513113458952,0.5565369335340675,0.5118196340084307,0.024506273144865753,0.021283852056777703,89084.88026764008,537.8274695033359,10.0,5,4,4,38,1467.9,29.1,32.4,0.7098039215686275,0.23742574183295365
+CAT (default),190.03885268302784,0.2709793741147243,87.82759080296049,0.13562630112555335,0.643860524899134,0.6196674016579227,0.07608253429058649,0.052193395777049297,428.26912869207973,7.186903437353133,12.911764705882353,0.21142,28.268621895048355,0.1854714552561442,6.827020885706225,0.08026752106160412,0.6746502933037238,0.6538434834236965,0.040283724715528546,0.024042640881410785,113.47556087857672,6.139565341592893,13.0,2,2,2,45,1458.7,30.1,27.7,0.7022058823529411,0.15094934971503085
+XGB (tuned),6066.175009788823,1.6190793797341305,1229.0991373608288,0.7004875821871327,0.7007828758839686,0.656471544613785,0.08021422165682039,0.06483692415176416,14800.692905333448,40.17091923504828,13.852941176470589,0.21499,2256.9276883072325,0.41774741808573407,693.4907982506384,0.3083513292273418,0.7223432295573234,0.6699580711692178,0.039851558091928974,0.03359463160650185,11769.825378159274,12.136706817415504,13.0,0,0,0,51,1435.4,26.7,30.2,0.6786764705882353,0.08553630288392573
+MNCA (tuned),50831.51192726024,15.584971993005873,6035.4844365627405,1.7226505575752833,0.7032607657750378,0.616689223742635,0.07567762156992885,0.06400824126147407,107380.48324313454,104.77227719302421,14.009803921568627,0.21328,14486.050127214856,0.585451708899604,4621.665633563503,0.4747242314576746,0.7724401098237351,0.6447177773449706,0.044069196843860126,0.04711164679001383,89084.88026764008,26.6476237825924,14.0,2,3,2,44,1428.6,28.3,19.8,0.6747549019607844,0.148986454489082
+REALMLP (tuned),73311.83245680422,0.765440520637695,6156.508467345906,0.43160487228962463,0.7615648419724279,0.688207302114276,0.08722617712339523,0.074188574366866,177178.04293492812,13.697358431538635,16.372549019607842,0.215,23874.98158947627,0.26132775147755943,6044.758795122033,0.18050895994575464,0.8260416027840659,0.7037015717498039,0.049718079859661035,0.03476200489314026,128255.5951822475,11.19862181751071,15.0,0,0,2,49,1372.7,29.4,28.2,0.615686274509804,0.08404632995980613
+TABM (default),139.62633961878052,1.0737770921524312,22.4928837749369,0.40818286049864,0.7249223512608948,0.7011689052436787,0.09327453272880078,0.09342522224307015,289.28104127940037,12.833742622244758,16.5,0.21188,40.332407061258955,0.18133597903781468,10.492230631952996,0.13221126395693922,0.8325407781946508,0.7101145169048528,0.04495971412156341,0.03430523183690445,186.77830789084513,10.724711035853641,16.0,0,1,0,50,1373.2,30.3,28.3,0.6125,0.09325166078098776
+NN_TORCH (tuned + ensemble),29590.123960411,13.183612702147375,3729.842332819238,3.5618312259496654,0.7913423906704912,0.7497717151970622,0.09005636083671732,0.08177089153065253,97942.50427646744,194.194370742189,17.294117647058822,0.21479,10848.660208092795,3.946354971991645,2874.6743506773596,1.9516254299583915,0.9196144626274988,0.8091881095168137,0.057918188893721245,0.04800420908274404,61441.834728019065,144.9992483814283,17.0,0,1,0,50,1351.7,22.5,28.1,0.5926470588235294,0.08512462431139158
+MNCA (default),252.05720278889527,8.333707038145958,17.796029105655933,1.1526772129094414,0.8466123430768121,0.7782944440883417,0.11219494516011082,0.0884997718557035,329.3551130182264,62.49957238602975,18.705882352941178,0.21885,36.0034454398685,0.5316168732113309,14.869495839530392,0.30768591310277943,1.0,0.8560745564571833,0.05667068743276982,0.05396804733520216,236.99495784925895,20.43419277465042,20.0,1,0,0,50,1319.3,25.0,22.6,0.5573529411764706,0.09306806365699362
+TABDPT (default),166.18249968905855,63.11337411850366,27.980557312145997,23.24231264879125,0.7067604944091354,0.6787597108860185,0.1077142126086124,0.08676756731636276,577.6877537613715,1486.1376930964866,18.764705882352942,0.22562,99.10453534126282,28.39870807859633,22.52690614988171,8.550738306685618,1.0,0.8430700103282259,0.0458913996174477,0.031816577640631366,528.528670151851,1255.434427440434,22.0,9,1,2,39,1317.6,34.0,25.7,0.5558823529411765,0.24600748397065467
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+FASTAI (tuned + ensemble),6629.648996400055,16.16286987151975,1343.5604508466745,7.704229376998794,0.847763610444884,0.8192378884582732,0.1280772560437908,0.10682866114025558,22455.399300542234,425.24771894393024,20.84313725490196,0.21679,3182.895098288854,10.795994228786892,593.237788402893,4.466873745216533,1.0,0.9481484787714531,0.07214417888217062,0.059212630303949465,19851.125229101002,409.94392325616803,22.5,0,1,2,48,1269.7,29.8,28.3,0.503921568627451,0.08034631734787774
+XT (tuned + ensemble),1289.1519403179227,3.0578905248434194,464.9314949446578,1.2901947647379284,0.8827760158511792,0.8396909000564725,0.12954079138309096,0.10995331462599778,5501.829777288316,78.94715148525705,21.372549019607842,0.21814,763.5855970117781,1.8364481396145291,183.01944048073585,0.761281055543471,1.0,0.9539534608972561,0.07457413317133521,0.06267735565102701,3537.1622158448404,68.20231667792302,24.0,0,0,3,48,1257.1,28.1,28.1,0.4906862745098039,0.07834090101915143
+EBM (tuned),34026.536489860475,0.16189759790507796,5478.959788432886,0.07139558038617727,0.8740079837962136,0.8379891966210511,0.13616014326308232,0.1329545911800612,45019.152540237825,2.1906554232861546,21.431372549019606,0.21772,2925.6548613442314,0.0440410852432251,1331.6775166450918,0.022730636596679687,1.0,0.9131323197096801,0.08806871275364736,0.05883998273382683,17751.99098903195,1.2223545537568268,22.0,0,0,1,50,1255.6,23.1,25.4,0.4892156862745098,0.06726792471193889
+REALMLP (default),448.72095363529684,0.7614608424421488,36.551378391408186,0.4234937252271941,0.8980359281995941,0.8395383044225884,0.11120114953337384,0.10170779666511945,1038.880257668142,13.498920180198532,21.735294117647058,0.21553,142.2050400045183,0.2570535076989068,35.35800008725903,0.19140706459681192,1.0,0.9145611503199415,0.08431809961962611,0.06278250719469083,719.9745480267878,11.535269383553011,23.0,0,0,0,51,1249.3,26.3,25.9,0.48161764705882354,0.05674906895653699
+XGB (default),12.860019501050314,0.6666093941607507,3.1825529067488505,0.3000924886961127,0.8935881423667761,0.8416378104789131,0.11133944893771364,0.11101893700190776,40.716829559380564,17.42803351650834,21.833333333333332,0.21667,5.82192047437032,0.3283502260843913,1.9409389396340444,0.12262328807300621,1.0,0.9309644533653846,0.0851641387449773,0.05741943426265329,34.48133232859512,10.22021081972956,21.5,0,0,1,50,1246.8,24.3,31.0,0.4791666666666667,0.057339775655101725
+NN_TORCH (tuned),29590.123960411,0.7234875786018787,3729.842332819238,0.20874333848987678,0.8968088714508368,0.8408346389591811,0.11185498046304537,0.10553076909476344,97942.50427646744,10.830632864633353,22.029411764705884,0.21784,10848.660208092795,0.2631075382232666,2874.6743506773596,0.131112832826372,1.0,0.8983629507892926,0.07707637065160389,0.06734944583074608,61441.834728019065,8.236695976463633,23.0,0,0,0,51,1240.5,29.7,24.1,0.4742647058823529,0.0548648405384145
+EBM (default),104.78526760316363,0.17165232525412033,11.079598604500092,0.09398863718788793,0.8753701223112293,0.8534095921423823,0.14635460063694122,0.1411111056948659,148.0901517975655,3.2508346897376326,23.0,0.21677,11.465454594294231,0.05977429548899333,4.6738599788414605,0.03961948198354664,1.0,0.9668495675431852,0.09424073545770384,0.05947657617617017,75.98904610132907,2.400460311914132,24.0,3,0,0,48,1219.5,25.1,26.2,0.45,0.10564503521194404
+GBM (default),8.619418829147072,1.0011564760166576,3.0462908189340183,0.25265600996978405,0.9255861200115366,0.8769150749775005,0.12054395704677265,0.10827112348591665,44.26353412937288,22.939896191571705,23.176470588235293,0.21706,5.971681065029568,0.2858244842953152,1.9600326879612946,0.14173548842042513,1.0,0.9228303733189597,0.09113184479689307,0.06431302282850372,32.72231234173676,8.612355874421967,23.0,0,0,0,51,1213.4,26.2,25.9,0.4455882352941177,0.047813328248814776
+XT (tuned),1289.1519403179227,0.3436224609158917,464.9314949446578,0.1744826726067124,0.9078855110953847,0.8739049195200678,0.14066728588722457,0.11970990910282847,5501.829777288316,9.97674146406182,23.294117647058822,0.21915,763.5855970117781,0.19129647148980033,183.01944048073585,0.09120693679998855,1.0,0.9787971295350696,0.08494158558463882,0.07201944622991324,3537.1622158448404,8.867381424714122,27.0,0,1,0,50,1209.6,27.2,27.3,0.4426470588235294,0.06759055036499491
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+NN_TORCH (default),74.85835825680128,0.5412627521423472,14.695998951842784,0.19921856017345402,0.9806438127346997,0.9515909665803707,0.1620847222292403,0.15205391708922938,327.51821759182326,9.71084645377221,28.65686274509804,0.22759,34.192402362823486,0.22645958264668783,9.990997226772679,0.1258046787872722,1.0,1.0,0.12442133778369147,0.10251098454201864,204.15320945265722,7.844934898940183,29.0,0,0,0,51,1070.4,23.0,24.8,0.30857843137254903,0.03807980911291169
+FASTAI (default),27.993367113578813,1.070063710732138,4.951242935164595,0.5056871108143457,0.980597127305428,0.953088543036155,0.19826288690738417,0.1907306783039593,87.08936574190912,29.523324076097715,30.470588235294116,0.24127,12.973222759034899,0.8480017715030246,2.8561126039251374,0.37317813888351864,1.0,1.0,0.15050538424095006,0.14150141420291248,77.36570184770021,28.70383139894662,33.0,0,0,0,51,1014.0,25.6,27.4,0.26323529411764707,0.03579216187562658
+RF (default),5.289521401640116,0.18747803105248345,0.8914167974682212,0.07557012377860906,0.9921615843984544,0.9665274322030536,0.20072931494064547,0.21489407007492525,11.63160057311077,4.814878723410724,30.862745098039216,0.2484,1.2628253036075168,0.08775801128811306,0.43425701731008426,0.05354175385774865,1.0,1.0,0.14295610983302143,0.12316470752268181,7.010079512208331,3.9076874559468244,32.0,0,0,0,51,1000.0,0.0,0.0,0.2534313725490196,0.03431062861608254
+XT (default),3.067954011524425,0.20851136391458946,0.7599985259503769,0.07668050932017083,0.976846527522819,0.95762955963915,0.21975063089427654,0.2346925990623719,6.922794409042171,5.082769758447109,31.53921568627451,0.24428,1.029017792807685,0.0932659043206109,0.2473449527431567,0.04980480471851431,1.0,1.0,0.17351668887004712,0.13674733154934707,5.707021421143385,4.456198846093199,34.0,0,0,0,51,977.3,22.3,29.0,0.23651960784313725,0.03600270779735502
+LR (tuned + ensemble),298.0772925112502,1.5473658189794337,106.92655310814473,0.5272909274052285,0.9690155979901051,0.9604101018326597,0.2964561838358268,0.358017663742451,1326.7185005565484,20.631703511708864,33.01960784313726,0.25172,171.24826147821216,0.28876688745286727,47.49214683366935,0.1676693138737952,1.0,1.0,0.23206264161510082,0.2393082389556497,1064.131515124883,12.175213508812917,36.0,0,0,1,50,923.5,28.4,25.8,0.19950980392156864,0.0397840394514003
+LR (tuned),298.0772925112502,0.41952294077488855,106.92655310814473,0.14529639602271893,0.9805057099085136,0.9657886847720463,0.3043853512990743,0.367315025593031,1326.7185005565484,5.927745129039777,33.94117647058823,0.25255,171.24826147821216,0.10993631680806477,47.49214683366935,0.0665447885938415,1.0,1.0,0.24354532900807213,0.2575122541077485,1064.131515124883,4.067640041721783,37.0,0,0,1,50,885.6,25.8,28.3,0.17647058823529413,0.03685974160525651
+LR (default),6.912347784956555,0.4444732105550163,2.521765696020704,0.1600876370747322,0.9855950536843827,0.9704218334420531,0.31958653451500185,0.4128363986558877,32.35021650084229,7.018103298699637,34.450980392156865,0.2546,5.298751910527547,0.12218634287516277,1.5162047581506444,0.0887949061495271,1.0,1.0,0.26933687033179554,0.2880686130933437,22.991652687961256,4.725399415979996,37.5,1,0,0,50,864.4,30.3,29.5,0.16372549019607843,0.04873304319530605
+KNN (tuned + ensemble),185.73769010095035,9.265668427242952,28.077306593709025,0.6763553759282402,1.0,0.9949115115995666,0.447040608541432,0.6075041958882814,80.584480435074,64.66285269534394,37.53921568627451,0.34658,12.91673379474216,0.27287014325459796,3.260792818960679,0.17676389939136214,1.0,1.0,0.4055189293595234,0.6658931347136859,60.87290895584066,13.90517184260326,39.0,0,0,0,51,686.9,38.4,30.9,0.08651960784313725,0.02684707640182819
+KNN (tuned),185.73769010095035,1.4108907164571591,28.077306593709025,0.11144693589219826,1.0,0.9978177275394109,0.46275440863389483,0.6502357234318229,80.584480435074,10.182721712640424,38.53921568627451,0.35012,12.91673379474216,0.08784447775946723,3.260792818960679,0.03624739765555662,1.0,1.0,0.420693335539855,0.740394718900499,60.87290895584066,2.2791618782574123,40.0,0,0,0,51,608.2,37.7,43.3,0.06151960784313725,0.026046267610479377
+KNN (default),1.8202055170645122,0.18718713898544476,0.4967972483415847,0.03640307146637206,1.0,0.9999536720947403,0.5365866131716529,0.9156894938911053,1.035103875827835,2.1647566343855695,39.96078431372549,0.40859,0.22567404641045463,0.036888705359564886,0.050692503527237594,0.021942545900661376,1.0,1.0,0.5109534123207524,1.0,1.0,1.2564234767690339,41.0,0,0,0,51,449.2,45.1,71.3,0.025980392156862746,0.025126134267953678
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diff --git a/data/lite/full-cls/leaderboard.tex b/data/lite/full-cls/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..ca4d53e34776dd65bdc5f3d0ea7d5cc133f0ebb0
--- /dev/null
+++ b/data/lite/full-cls/leaderboard.tex
@@ -0,0 +1,49 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+AutoGluon 1.3 (4h) & \textcolor{gold}{\textbf{1537${}_{-39,+35}$}} & \textcolor{gold}{\textbf{0.564}} & \textcolor{gold}{\textbf{7.2}} & \textcolor{gold}{\textbf{2.9}} & \textcolor{gold}{\textbf{7}} & \textcolor{gold}{\textbf{6.2\%}} & 1355.34 & 1.98 \\
+TabM (T+E) & \textcolor{silver}{\textbf{1435${}_{-28,+34}$}} & \textcolor{silver}{\textbf{0.492}} & \textcolor{silver}{\textbf{10.6}} & \textcolor{silver}{\textbf{4.4}} & \textcolor{bronze}{\textbf{3}} & \textcolor{silver}{\textbf{9.3\%}} & 2461.81 & 1.28 \\
+RealMLP (T+E) & \textcolor{bronze}{\textbf{1427${}_{-29,+34}$}} & \textcolor{bronze}{\textbf{0.419}} & \textcolor{bronze}{\textbf{10.7}} & 6.1 & 1 & 9.9\% & 5463.43 & 3.35 \\
+LightGBM (T+E) & 1420${}_{-29,+33}$ & 0.404 & 10.9 & 7.9 & 0 & 10.8\% & 391.23 & 1.24 \\
+XGBoost (T+E) & 1392${}_{-30,+30}$ & 0.321 & 12.2 & 5.9 & 2 & 11.3\% & 682.62 & 0.97 \\
+CatBoost (T+E) & 1387${}_{-19,+29}$ & 0.379 & 12.3 & 7.2 & 0 & \textcolor{bronze}{\textbf{9.8\%}} & 1326.58 & 0.53 \\
+CatBoost (T) & 1371${}_{-29,+28}$ & 0.365 & 13.0 & 8.4 & 0 & 10.4\% & 1326.58 & 0.05 \\
+CatBoost (D) & 1367${}_{-27,+32}$ & 0.345 & 13.2 & 7.4 & 2 & 10.8\% & 5.74 & 0.11 \\
+TabM (T) & 1366${}_{-22,+29}$ & 0.407 & 13.1 & 5.4 & 2 & 10.3\% & 2461.81 & 0.14 \\
+ModernNCA (T) & 1360${}_{-25,+38}$ & 0.278 & 13.4 & 5.9 & 2 & 10.3\% & 4853.07 & 0.47 \\
+ModernNCA (T+E) & 1355${}_{-22,+34}$ & 0.373 & 13.7 & \textcolor{bronze}{\textbf{5.1}} & 2 & 10.2\% & 4853.07 & 9.39 \\
+LightGBM (T) & 1352${}_{-24,+30}$ & 0.318 & 13.8 & 9.4 & 0 & 11.8\% & 391.23 & 0.19 \\
+XGBoost (T) & 1351${}_{-23,+32}$ & 0.266 & 13.7 & 7.5 & 1 & 11.6\% & 682.62 & 0.18 \\
+TabM (D) & 1296${}_{-36,+30}$ & 0.271 & 16.3 & 7.7 & 1 & 13.9\% & 10.31 & 0.14 \\
+RealMLP (T) & 1278${}_{-25,+33}$ & 0.202 & 17.0 & 11.4 & 0 & 13.8\% & 5463.43 & 0.19 \\
+TorchMLP (T+E) & 1276${}_{-27,+26}$ & 0.222 & 17.2 & 9.6 & 1 & 13.0\% & 2480.32 & 2.18 \\
+EBM (T+E) & 1276${}_{-24,+28}$ & 0.180 & 17.1 & 7.2 & 2 & 15.3\% & 1011.59 & 0.22 \\
+EBM (T) & 1236${}_{-23,+32}$ & 0.122 & 18.9 & 9.2 & 1 & 16.0\% & 1011.59 & 0.02 \\
+EBM (D) & 1206${}_{-29,+29}$ & 0.136 & 20.5 & 9.8 & 1 & 17.0\% & 5.44 & 0.06 \\
+FastaiMLP (T+E) & 1202${}_{-21,+29}$ & 0.196 & 20.5 & 11.5 & 0 & 17.0\% & 622.79 & 5.04 \\
+XGBoost (D) & 1200${}_{-29,+28}$ & 0.105 & 20.6 & 11.4 & 1 & 14.8\% & 1.88 & 0.12 \\
+ExtraTrees (T+E) & 1192${}_{-25,+32}$ & 0.104 & 21.0 & 11.8 & 0 & 16.7\% & 197.16 & 0.72 \\
+TorchMLP (T) & 1190${}_{-25,+32}$ & 0.096 & 21.0 & 15.2 & 0 & 15.4\% & 2480.32 & 0.12 \\
+RealMLP (D) & 1181${}_{-21,+34}$ & 0.090 & 21.4 & 11.8 & 1 & 15.5\% & 34.76 & 0.19 \\
+ModernNCA (D) & 1176${}_{-29,+28}$ & 0.134 & 21.8 & 11.2 & 1 & 17.2\% & 13.35 & 0.35 \\
+RandomForest (T+E) & 1164${}_{-28,+31}$ & 0.107 & 22.3 & 10.6 & 1 & 17.0\% & 327.31 & 0.71 \\
+ExtraTrees (T) & 1155${}_{-26,+33}$ & 0.079 & 22.6 & 10.3 & 0 & 17.8\% & 197.16 & 0.08 \\
+LightGBM (D) & 1150${}_{-33,+27}$ & 0.076 & 23.1 & 19.0 & 0 & 16.5\% & 2.25 & 0.16 \\
+TabDPT (D) & 1137${}_{-29,+35}$ & 0.176 & 23.4 & 6.5 & \textcolor{silver}{\textbf{4}} & 17.6\% & 22.39 & 8.59 \\
+FastaiMLP (T) & 1128${}_{-26,+30}$ & 0.089 & 24.0 & 14.4 & 0 & 19.0\% & 622.79 & 0.35 \\
+RandomForest (T) & 1116${}_{-24,+34}$ & 0.076 & 24.4 & 16.8 & 0 & 17.9\% & 327.31 & 0.08 \\
+TorchMLP (D) & 1020${}_{-28,+34}$ & 0.021 & 28.5 & 24.9 & 0 & 21.5\% & 5.73 & 0.15 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.011 & 29.2 & 22.8 & 0 & 24.9\% & 0.37 & 0.04 \\
+FastaiMLP (D) & 990${}_{-30,+32}$ & 0.026 & 29.5 & 25.7 & 0 & 23.2\% & 3.12 & 0.33 \\
+ExtraTrees (D) & 928${}_{-38,+31}$ & 0.010 & 31.6 & 27.6 & 0 & 27.9\% & 0.24 & 0.04 \\
+Linear (T+E) & 927${}_{-25,+35}$ & 0.042 & 31.7 & 17.3 & 1 & 31.3\% & 51.96 & 0.17 \\
+Linear (T) & 893${}_{-27,+31}$ & 0.026 & 32.6 & 25.7 & 0 & 32.1\% & 51.96 & 0.06 \\
+Linear (D) & 869${}_{-35,+29}$ & 0.019 & 33.4 & 31.1 & 0 & 33.0\% & 1.62 & 0.09 \\
+KNN (T+E) & 745${}_{-33,+34}$ & 0.000 & 36.3 & 29.9 & 0 & 48.4\% & 3.51 & 0.16 \\
+KNN (T) & 678${}_{-48,+33}$ & 0.000 & 37.5 & 25.8 & 0 & 50.5\% & 3.51 & 0.04 \\
+KNN (D) & 467${}_{-52,+55}$ & 0.000 & 39.8 & 39.4 & 0 & 58.9\% & 0.07 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/lite/full-cls/tabarena_leaderboard.csv b/data/lite/full-cls/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..67b9caa6ae8057b8e1a4e394cd2a562815cb1f8e
--- /dev/null
+++ b/data/lite/full-cls/tabarena_leaderboard.csv
@@ -0,0 +1,42 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+AutoGluon 1.3 (4h),7774.991528053033,19.875839289865997,2811.0795427294815,3.0481623340146933,0.4357260444094882,0.3637085860567527,0.06245100760478425,0.03653837673944927,29846.24559964093,265.3700814985468,7.197368421052632,0.157955,7014.845859646797,3.7256141901016235,1355.3359335244184,1.9841131308583402,0.3593825434326649,0.3030096923956198,0.03219178172950288,0.012606181703571726,21384.37692728419,133.53161235412554,6.0,7,5,2,24,1536.6,34.7,38.4,0.8450657894736842,0.3447152162656293
+TABM (tuned + ensemble),34310.62852196317,8.826874965115598,4300.923574958667,3.470904002174896,0.5077918677314276,0.4928011834178911,0.09320926958157053,0.07730800937457659,45131.79918122951,135.34700738700144,10.578947368421053,0.174405,8260.626611948013,3.27972948551178,2461.8116026347016,1.282862647131695,0.47136645123500964,0.48985326453336486,0.043573580667432665,0.023331148793904504,37327.19260107514,113.73588992793104,8.0,3,2,5,28,1434.7,33.1,27.1,0.7605263157894737,0.22504512519953232
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+RF (default),4.080644030320017,0.13749531068299947,0.8126703096595244,0.06965281933938666,0.9894800211663467,0.9674716972377848,0.24916098049052274,0.23883179708413727,6.74726637377606,4.2459113559317,29.157894736842106,0.20493,1.552122712135315,0.09239459037780762,0.37380590277783665,0.037675282187559014,1.0,1.0,0.22032371855138488,0.13407694191427183,5.06536582970867,3.354866759305418,31.75,0,0,1,37,1000.0,0.0,0.0,0.29605263157894735,0.043926004795501065
+FASTAI (default),30.567647507316188,1.0886577556007786,5.142705410747499,0.514750514881177,0.9739593024362323,0.9433285405953568,0.23208065955007007,0.1822497405417556,72.5941463445461,31.969331116236056,29.473684210526315,0.187165,14.332883596420288,0.6891474723815918,3.1247662358290373,0.33408521032303745,1.0,1.0,0.16181477069756522,0.12140229652210177,51.9702290983138,26.158132978083096,31.0,0,0,0,38,990.5,32.0,29.2,0.2881578947368421,0.03886887345261015
+XT (default),2.8151443882992395,0.17746856965516744,0.7494419232366839,0.0764609601145254,0.9898367767783371,0.9716632164859568,0.2793547475052598,0.2725382456849271,4.797654893597667,4.664132317562142,31.63157894736842,0.20700000000000002,0.9193735122680664,0.08453762531280518,0.24344244584214264,0.037069730458521875,1.0,1.0,0.2174961946642111,0.15156553071339784,3.5050999047562326,4.1617612391934,34.5,0,0,0,38,927.6,30.2,37.2,0.23421052631578948,0.036229169693596776
+LR (tuned + ensemble),310.8921214467601,1.6511000143854242,112.04387665947259,0.6061180299813982,0.95841567098672,0.9708437365552002,0.31310270156620607,0.28149705038959444,1051.5597600046694,27.942607605611126,31.657894736842106,0.20664,172.39098751544952,0.3049362897872925,51.96211560156496,0.1713220944305931,1.0,1.0,0.2560382503294761,0.1682426614669617,652.0216614396725,8.851961834833686,35.0,1,0,0,37,927.0,34.9,24.7,0.23355263157894737,0.057640312921943146
+LR (tuned),310.8921214467601,0.5151690432899877,112.04387665947259,0.17414856714912683,0.9738366106666893,0.97980647335986,0.32065351674271636,0.2951636582555988,1051.5597600046694,7.373474320542152,32.63157894736842,0.206755,172.39098751544952,0.14061975479125977,51.96211560156496,0.06357160024321462,1.0,1.0,0.2713987757406089,0.1825065086765385,652.0216614396725,5.062717123136234,35.5,0,0,1,37,893.4,30.7,26.1,0.20921052631578949,0.03888425407679143
+LR (default),7.7264530345013265,0.5259399602287694,2.6686237618793442,0.19925514469367503,0.9806670457343031,0.9860341079029044,0.33047877847380847,0.3220913721066172,27.432135439923183,8.872452564577243,33.36842105263158,0.21439,5.452793717384338,0.1415010690689087,1.6174976146916054,0.09387864407044556,1.0,1.0,0.27139753987310355,0.1831054339424733,15.972794808418932,5.719143257720329,36.0,0,0,0,38,869.4,28.8,34.9,0.19078947368421054,0.03212487482338037
+KNN (tuned + ensemble),212.22012870562705,8.11043459490726,28.850708739248347,0.6076026284446762,1.0,0.9828353904168845,0.48361351363956845,0.5911588168624139,79.58293822496161,51.24023018425664,36.31578947368421,0.320445,18.52320122718811,0.22250914573669434,3.5145795148857952,0.1617064055034519,1.0,1.0,0.4081435434925469,0.6872102333351288,56.97784214902664,12.39378247919743,39.0,0,0,0,38,744.6,34.0,32.3,0.11710526315789474,0.0334880126619938
+KNN (tuned),212.22012870562705,1.883336155038131,28.850708739248347,0.13054448296641816,1.0,0.9845999552108321,0.5047438562255071,0.6412798413840507,79.58293822496161,11.122407039906847,37.51315789473684,0.32193499999999997,18.52320122718811,0.07584631443023682,3.5145795148857952,0.03818510847996826,1.0,1.0,0.48517378670484473,0.7165338507811103,56.97784214902664,2.1231636318644824,40.0,0,1,0,37,677.7,32.4,47.2,0.08717105263157894,0.038768005188542266
+KNN (default),1.74290458779586,0.21126952296809146,0.5001806322977429,0.036905984979792666,1.0,1.0,0.5890502564791692,0.9411577191145609,1.0024208997554083,2.3365493404640363,39.828947368421055,0.37738,0.3047138452529907,0.03128814697265625,0.06855464474199147,0.019133871606833427,1.0,1.0,0.5282941392408126,1.0,1.0,1.2278781340930796,41.0,0,0,0,38,467.1,55.0,51.4,0.029276315789473685,0.02541007993534458
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diff --git a/data/lite/full-reg/leaderboard.tex b/data/lite/full-reg/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..155cc043844ee2e04bb19ccfde7e858c639feca1
--- /dev/null
+++ b/data/lite/full-reg/leaderboard.tex
@@ -0,0 +1,49 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+AutoGluon 1.3 (4h) & \textcolor{gold}{\textbf{1881${}_{-87,+73}$}} & \textcolor{gold}{\textbf{0.735}} & \textcolor{gold}{\textbf{3.6}} & \textcolor{gold}{\textbf{2.1}} & \textcolor{silver}{\textbf{3}} & \textcolor{silver}{\textbf{2.4\%}} & 1734.20 & 7.06 \\
+RealMLP (T+E) & \textcolor{silver}{\textbf{1758${}_{-56,+70}$}} & \textcolor{silver}{\textbf{0.734}} & \textcolor{silver}{\textbf{5.6}} & 3.4 & 1 & \textcolor{gold}{\textbf{2.1\%}} & 6534.34 & 2.30 \\
+ModernNCA (T+E) & \textcolor{bronze}{\textbf{1675${}_{-55,+60}$}} & 0.603 & \textcolor{bronze}{\textbf{7.5}} & \textcolor{silver}{\textbf{2.3}} & \textcolor{gold}{\textbf{4}} & 4.3\% & 3811.43 & 7.58 \\
+LightGBM (T+E) & 1657${}_{-49,+62}$ & 0.531 & 7.8 & 5.9 & 0 & 4.9\% & 686.46 & 5.48 \\
+TabDPT (D) & 1632${}_{-52,+57}$ & \textcolor{bronze}{\textbf{0.635}} & 8.6 & \textcolor{bronze}{\textbf{2.9}} & \textcolor{silver}{\textbf{3}} & \textcolor{bronze}{\textbf{2.7\%}} & 16.97 & 8.70 \\
+CatBoost (T+E) & 1615${}_{-48,+53}$ & 0.536 & 9.2 & 7.0 & 0 & 4.6\% & 2895.38 & 1.32 \\
+TabM (T+E) & 1583${}_{-57,+59}$ & 0.488 & 10.2 & 5.2 & 1 & 3.1\% & 4228.53 & 1.19 \\
+CatBoost (T) & 1569${}_{-51,+52}$ & 0.512 & 10.7 & 5.6 & 0 & 4.7\% & 2895.38 & 0.07 \\
+LightGBM (T) & 1542${}_{-49,+40}$ & 0.432 & 11.5 & 9.1 & 0 & 5.7\% & 686.46 & 0.74 \\
+XGBoost (T) & 1504${}_{-54,+53}$ & 0.398 & 12.7 & 10.9 & 0 & 5.6\% & 848.99 & 0.47 \\
+XGBoost (T+E) & 1496${}_{-50,+62}$ & 0.432 & 12.8 & 11.6 & 0 & 5.6\% & 848.99 & 2.38 \\
+ModernNCA (D) & 1490${}_{-45,+61}$ & 0.209 & 13.0 & 9.3 & 0 & 7.1\% & 16.07 & 0.29 \\
+TabM (T) & 1484${}_{-56,+48}$ & 0.380 & 13.3 & 8.9 & 0 & 3.9\% & 4228.53 & 0.13 \\
+RealMLP (T) & 1452${}_{-50,+48}$ & 0.344 & 14.5 & 11.0 & 0 & 4.9\% & 6534.34 & 0.07 \\
+CatBoost (D) & 1442${}_{-53,+63}$ & 0.389 & 14.6 & 10.6 & 0 & 6.9\% & 8.35 & 0.09 \\
+ModernNCA (T) & 1424${}_{-41,+60}$ & 0.351 & 15.3 & 6.4 & 0 & 7.0\% & 3811.43 & 0.45 \\
+TabM (D) & 1366${}_{-44,+56}$ & 0.287 & 17.2 & 14.0 & 0 & 5.7\% & 13.90 & 0.12 \\
+TorchMLP (T+E) & 1324${}_{-45,+43}$ & 0.171 & 18.9 & 16.0 & 0 & 7.0\% & 4452.11 & 0.85 \\
+RealMLP (D) & 1306${}_{-52,+53}$ & 0.137 & 19.4 & 11.2 & 0 & 7.1\% & 32.24 & 0.08 \\
+ExtraTrees (T+E) & 1301${}_{-51,+55}$ & 0.157 & 19.6 & 12.3 & 0 & 10.1\% & 161.73 & 0.78 \\
+ExtraTrees (T) & 1278${}_{-53,+54}$ & 0.129 & 20.5 & 11.6 & 0 & 10.5\% & 161.73 & 0.12 \\
+TorchMLP (T) & 1262${}_{-48,+58}$ & 0.123 & 21.1 & 17.2 & 0 & 7.6\% & 4452.11 & 0.09 \\
+LightGBM (D) & 1224${}_{-45,+47}$ & 0.070 & 22.2 & 21.2 & 0 & 8.6\% & 2.03 & 0.30 \\
+RandomForest (T+E) & 1196${}_{-52,+45}$ & 0.066 & 23.5 & 20.5 & 0 & 11.2\% & 526.17 & 0.77 \\
+XGBoost (D) & 1188${}_{-54,+61}$ & 0.111 & 23.7 & 22.0 & 0 & 9.4\% & 2.15 & 0.18 \\
+RandomForest (T) & 1137${}_{-52,+53}$ & 0.048 & 25.2 & 22.7 & 0 & 12.0\% & 526.17 & 0.12 \\
+EBM (T+E) & 1135${}_{-51,+69}$ & 0.160 & 25.2 & 15.5 & 0 & 13.8\% & 2124.78 & 0.12 \\
+ExtraTrees (D) & 1112${}_{-46,+55}$ & 0.061 & 26.0 & 22.7 & 0 & 12.1\% & 0.42 & 0.06 \\
+FastaiMLP (T+E) & 1093${}_{-40,+60}$ & 0.023 & 26.5 & 25.6 & 0 & 12.1\% & 527.21 & 2.83 \\
+EBM (T) & 1085${}_{-66,+59}$ & 0.137 & 27.1 & 9.0 & 1 & 14.4\% & 2124.78 & 0.01 \\
+TorchMLP (D) & 1077${}_{-59,+60}$ & 0.016 & 27.4 & 25.4 & 0 & 11.9\% & 20.50 & 0.08 \\
+FastaiMLP (T) & 1048${}_{-54,+48}$ & 0.013 & 27.9 & 27.3 & 0 & 12.6\% & 527.21 & 0.31 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.000 & 29.3 & 28.7 & 0 & 13.3\% & 0.63 & 0.06 \\
+EBM (D) & 999${}_{-56,+50}$ & 0.093 & 29.4 & 27.0 & 0 & 15.1\% & 7.25 & 0.04 \\
+FastaiMLP (D) & 919${}_{-60,+50}$ & 0.000 & 31.1 & 30.6 & 0 & 17.0\% & 3.08 & 0.29 \\
+KNN (T+E) & 488${}_{-125,+81}$ & 0.000 & 37.1 & 36.9 & 0 & 36.8\% & 2.25 & 0.15 \\
+Linear (T+E) & 467${}_{-81,+69}$ & 0.000 & 37.3 & 37.2 & 0 & 34.9\% & 46.50 & 0.14 \\
+Linear (T) & 397${}_{-92,+86}$ & 0.000 & 37.9 & 37.8 & 0 & 35.1\% & 46.50 & 0.04 \\
+KNN (T) & 369${}_{-79,+103}$ & 0.000 & 38.2 & 38.1 & 0 & 37.4\% & 2.25 & 0.03 \\
+Linear (D) & 284${}_{-112,+94}$ & 0.000 & 39.0 & 39.0 & 0 & 37.6\% & 1.16 & 0.08 \\
+KNN (D) & 204${}_{-107,+107}$ & 0.000 & 39.7 & 39.6 & 0 & 41.1\% & 0.04 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/lite/full-reg/tabarena_leaderboard.csv b/data/lite/full-reg/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3127f0f93cd53b0fe3d96efbd385640ea1ae6ab2
--- /dev/null
+++ b/data/lite/full-reg/tabarena_leaderboard.csv
@@ -0,0 +1,42 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+AutoGluon 1.3 (4h),10051.83785378016,70.68019696382376,3153.0175193360524,8.747021690702612,0.26461131829744206,0.23246284524554242,0.023916771631798464,0.02739153218756116,77389.85490429384,1642.993680810643,3.6153846153846154,4.10013,12458.540483474731,10.641454219818115,1734.201033855079,7.062674608341483,0.23133371369265832,0.15318450222486296,0.009595256724279078,0.010747053668057768,76123.2860686676,448.5777143640419,3.0,3,3,2,5,1881.3,72.9,86.1,0.9346153846153846,0.46789044289044285
+REALMLP (tuned + ensemble),67720.63958248726,12.596411228179932,7060.621324893922,7.808561874535024,0.26596078198184286,0.28211068496756564,0.02079798687061351,0.0318369877671686,300508.62324682355,250.14082076713328,5.615384615384615,4.1572,28985.167714595795,4.047658205032349,6534.342759080719,2.2990216834440518,0.1939333280527935,0.22514913688996746,0.013728568811321784,0.014835698760319415,215284.61589654352,243.1358386691401,6.0,1,2,2,8,1758.0,69.5,55.9,0.8846153846153846,0.2927655677655677
+MNCA (tuned + ensemble),32095.7095044026,50.336935831950264,6167.970103891367,17.67280087259302,0.39661026996847265,0.32801771819090547,0.04309650922972409,0.03908136048505314,183828.00911751532,735.9936636946994,7.461538461538462,5.17584,16410.6605322361,9.919520378112793,3811.431210311487,7.580550796782489,0.35096562881515253,0.15722206903461022,0.013723617424411305,0.015091117740655323,215831.1755427445,518.4548592505826,4.0,4,1,0,8,1674.9,59.2,54.3,0.8384615384615385,0.43316990701606084
+GBM (tuned + ensemble),3436.114175393031,58.23533050830547,795.9694443599602,8.418828059060083,0.4687885136784251,0.4435140155179781,0.04872699145877461,0.048825649620474074,19731.156544048128,1434.220040412032,7.846153846153846,4.23184,3069.166275501251,10.259183883666992,686.4649311851701,5.476648655048637,0.41525976638432377,0.43203539999740836,0.025882078477524217,0.03357473090357181,19944.502036932092,444.7715795428446,6.0,0,0,2,11,1656.8,61.7,48.4,0.8288461538461539,0.1695818070818071
+TABDPT (default),148.7485225384052,53.04140243163476,27.664670515034977,22.751733349883246,0.36524485788247774,0.398418313824876,0.02707452489314851,0.042379631052937836,812.9269128730391,1906.3906090967916,8.615384615384615,4.24006,138.3429434299469,23.635958433151245,16.972548083255166,8.69507121915186,0.28825222902504594,0.2790724690964867,0.028230517433738833,0.02927377278034004,862.9680595038594,1818.2713070390416,6.0,3,0,1,9,1631.9,56.3,51.3,0.8096153846153846,0.34416985729202926
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+NN_TORCH (default),149.93631285887497,0.22603647525493914,24.53274626856709,0.08820389410472865,0.9842745385355711,0.9733305251076131,0.11891120219929098,0.19124345982678712,789.2682924687753,6.761490179412745,27.384615384615383,4.48918,79.02296853065491,0.1783456802368164,20.501721600246,0.08139732073405338,1.0,1.0,0.08808381765426243,0.127190652207085,464.3796175172265,6.690027290059689,26.0,0,0,0,13,1076.6,59.6,58.2,0.3403846153846154,0.03933724171166253
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+KNN (tuned + ensemble),130.0447974755214,2.3221586850973277,31.413578991094717,0.3839981934688547,1.0,1.0,0.36756485806297295,0.6734558517564605,86.62047407939465,27.45502773232453,37.07692307692308,8.16815,12.739671230316162,0.2766997814178467,2.253836358499806,0.14740763433303566,1.0,1.0,0.4085005377667177,0.797964481423868,68.17976708247701,13.891874242000187,38.0,0,0,0,13,488.5,80.2,124.7,0.09807692307692308,0.027092595293357377
+LR (tuned + ensemble),259.4397970529703,0.5564901095170242,90.58997126979446,0.25975101191673244,1.0,1.0,0.3493121067236557,0.6282022661768178,1967.1950270790398,11.602054705435593,37.26923076923077,7.90301,156.73520398139954,0.17154169082641602,46.49516183285896,0.14113267629856877,1.0,1.0,0.3712901272900253,0.6624491377852132,1676.6799766486058,9.252055102805237,36.0,0,0,0,13,467.2,68.9,80.1,0.09326923076923077,0.026875828301800728
+LR (tuned),259.4397970529703,0.12883753042954665,90.58997126979446,0.058721919597199,1.0,1.0,0.35117627377232247,0.637664314344675,1967.1950270790398,2.9663690915463836,37.92307692307692,7.95008,156.73520398139954,0.04031491279602051,46.49516183285896,0.04248926416039467,1.0,1.0,0.3720471503072048,0.6706483938757745,1676.6799766486058,3.0754592783196695,38.0,0,0,0,13,397.0,85.2,91.5,0.07692307692307693,0.026438318069682023
+KNN (tuned),130.0447974755214,0.2600700121659499,31.413578991094717,0.054814694748458494,1.0,1.0,0.3742426273573644,0.7065181173770695,86.62047407939465,3.596722365416154,38.23076923076923,8.28075,12.739671230316162,0.11144423484802246,2.253836358499806,0.029449351131916046,1.0,1.0,0.41308029193402596,0.8249023799133182,68.17976708247701,2.325030564010288,39.0,0,0,0,13,369.3,102.6,78.8,0.06923076923076923,0.02623730508345893
+LR (default),4.9569381567148065,0.18706706854013297,2.04240300519074,0.0952681059723661,1.0,1.0,0.3762906108037262,0.7449242295439796,46.37076890234856,6.575927550408862,39.03846153846154,8.07907,5.157731294631958,0.10304999351501465,1.1643471259929907,0.0800294014177239,1.0,1.0,0.3720269301046141,0.7403523158287414,40.21502356299305,3.589990624937666,38.0,0,0,0,13,284.2,93.6,111.7,0.04903846153846154,0.025668142221415648
+KNN (default),2.3537445801955004,0.07271528244018555,0.6011442901693675,0.029873606599490884,1.0,1.0,0.4112787475186339,0.8492654887800806,1.1881523781374654,1.663589934493969,39.69230769230769,8.5423,0.15627789497375488,0.04499936103820801,0.03787513578449361,0.02182507289554364,1.0,1.0,0.4499088247272143,1.0,1.0,1.6403093647746019,41.0,0,0,0,13,203.6,107.0,106.1,0.032692307692307694,0.025282024351343992
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diff --git a/data/lite/full/leaderboard.tex b/data/lite/full/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..ecba59b431c65cfd55adcb50fc1875f6e65610c3
--- /dev/null
+++ b/data/lite/full/leaderboard.tex
@@ -0,0 +1,49 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+AutoGluon 1.3 (4h) & \textcolor{gold}{\textbf{1582${}_{-29,+36}$}} & \textcolor{gold}{\textbf{0.608}} & \textcolor{gold}{\textbf{6.3}} & \textcolor{gold}{\textbf{2.7}} & \textcolor{gold}{\textbf{10}} & \textcolor{gold}{\textbf{5.3\%}} & 1453.27 & 3.15 \\
+RealMLP (T+E) & \textcolor{silver}{\textbf{1479${}_{-34,+27}$}} & \textcolor{silver}{\textbf{0.499}} & \textcolor{silver}{\textbf{9.4}} & 5.1 & 2 & \textcolor{bronze}{\textbf{7.9\%}} & 6026.47 & 3.13 \\
+LightGBM (T+E) & \textcolor{bronze}{\textbf{1457${}_{-27,+30}$}} & 0.437 & \textcolor{bronze}{\textbf{10.2}} & 7.3 & 0 & 9.3\% & 416.56 & 2.24 \\
+TabM (T+E) & 1449${}_{-34,+24}$ & \textcolor{bronze}{\textbf{0.491}} & 10.5 & \textcolor{bronze}{\textbf{4.6}} & 4 & \textcolor{silver}{\textbf{7.7\%}} & 3133.91 & 1.27 \\
+CatBoost (T+E) & 1422${}_{-25,+30}$ & 0.419 & 11.5 & 7.2 & 0 & 8.4\% & 1665.53 & 0.56 \\
+ModernNCA (T+E) & 1406${}_{-32,+29}$ & 0.432 & 12.1 & \textcolor{silver}{\textbf{3.9}} & \textcolor{bronze}{\textbf{6}} & 8.7\% & 4618.50 & 7.74 \\
+CatBoost (T) & 1400${}_{-34,+20}$ & 0.403 & 12.4 & 7.4 & 0 & 9.0\% & 1665.53 & 0.07 \\
+XGBoost (T+E) & 1398${}_{-23,+28}$ & 0.349 & 12.4 & 6.8 & 2 & 9.8\% & 700.96 & 1.44 \\
+TabM (T) & 1381${}_{-32,+21}$ & 0.400 & 13.2 & 6.0 & 2 & 8.7\% & 3133.91 & 0.13 \\
+LightGBM (T) & 1381${}_{-20,+24}$ & 0.347 & 13.2 & 9.3 & 0 & 10.2\% & 416.56 & 0.38 \\
+XGBoost (T) & 1372${}_{-23,+28}$ & 0.299 & 13.5 & 8.1 & 1 & 10.1\% & 700.96 & 0.21 \\
+CatBoost (D) & 1370${}_{-32,+29}$ & 0.356 & 13.5 & 8.0 & 2 & 9.8\% & 6.70 & 0.09 \\
+ModernNCA (T) & 1363${}_{-27,+21}$ & 0.297 & 13.9 & 6.1 & 2 & 9.4\% & 4618.50 & 0.47 \\
+RealMLP (T) & 1308${}_{-29,+31}$ & 0.238 & 16.3 & 11.3 & 0 & 11.5\% & 6026.47 & 0.17 \\
+TabM (D) & 1301${}_{-19,+27}$ & 0.275 & 16.5 & 8.7 & 1 & 11.8\% & 11.56 & 0.13 \\
+TorchMLP (T+E) & 1277${}_{-26,+26}$ & 0.209 & 17.6 & 10.6 & 1 & 11.5\% & 2832.80 & 1.80 \\
+EBM (T+E) & 1243${}_{-24,+23}$ & 0.175 & 19.1 & 8.3 & 2 & 14.9\% & 1323.39 & 0.18 \\
+ModernNCA (D) & 1237${}_{-29,+21}$ & 0.153 & 19.6 & 10.7 & 1 & 14.6\% & 13.74 & 0.32 \\
+TabDPT (D) & 1233${}_{-27,+25}$ & 0.293 & 19.6 & 4.9 & \textcolor{silver}{\textbf{7}} & 13.8\% & 20.56 & 8.62 \\
+ExtraTrees (T+E) & 1209${}_{-27,+24}$ & 0.117 & 20.7 & 11.9 & 0 & 15.0\% & 191.44 & 0.76 \\
+RealMLP (D) & 1204${}_{-23,+23}$ & 0.102 & 20.9 & 11.6 & 1 & 13.4\% & 34.70 & 0.18 \\
+EBM (T) & 1201${}_{-30,+24}$ & 0.126 & 21.0 & 9.1 & 2 & 15.6\% & 1323.39 & 0.02 \\
+TorchMLP (T) & 1201${}_{-25,+25}$ & 0.103 & 21.0 & 15.6 & 0 & 13.4\% & 2832.80 & 0.11 \\
+XGBoost (D) & 1191${}_{-25,+32}$ & 0.106 & 21.4 & 13.0 & 1 & 13.4\% & 2.06 & 0.12 \\
+FastaiMLP (T+E) & 1179${}_{-27,+27}$ & 0.152 & 22.0 & 13.4 & 0 & 15.7\% & 594.95 & 4.65 \\
+ExtraTrees (T) & 1178${}_{-24,+22}$ & 0.092 & 22.1 & 10.6 & 0 & 15.9\% & 191.44 & 0.10 \\
+RandomForest (T+E) & 1167${}_{-27,+20}$ & 0.096 & 22.6 & 12.0 & 1 & 15.5\% & 377.08 & 0.75 \\
+EBM (D) & 1164${}_{-24,+24}$ & 0.125 & 22.7 & 11.7 & 1 & 16.5\% & 5.48 & 0.06 \\
+LightGBM (D) & 1162${}_{-33,+25}$ & 0.074 & 22.8 & 19.5 & 0 & 14.5\% & 2.20 & 0.17 \\
+RandomForest (T) & 1120${}_{-24,+21}$ & 0.069 & 24.6 & 17.9 & 0 & 16.4\% & 377.08 & 0.09 \\
+FastaiMLP (T) & 1109${}_{-22,+25}$ & 0.070 & 25.0 & 16.3 & 0 & 17.4\% & 594.95 & 0.34 \\
+TorchMLP (D) & 1029${}_{-27,+26}$ & 0.019 & 28.2 & 25.1 & 0 & 19.1\% & 8.96 & 0.13 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.008 & 29.2 & 24.0 & 0 & 22.0\% & 0.43 & 0.05 \\
+FastaiMLP (D) & 983${}_{-28,+23}$ & 0.019 & 29.9 & 26.8 & 0 & 21.6\% & 3.12 & 0.31 \\
+ExtraTrees (D) & 971${}_{-28,+28}$ & 0.023 & 30.2 & 26.2 & 0 & 23.9\% & 0.26 & 0.05 \\
+Linear (T+E) & 878${}_{-25,+30}$ & 0.031 & 33.1 & 20.1 & 1 & 32.2\% & 47.11 & 0.16 \\
+Linear (T) & 842${}_{-29,+31}$ & 0.019 & 34.0 & 28.0 & 0 & 32.8\% & 47.11 & 0.06 \\
+Linear (D) & 810${}_{-31,+29}$ & 0.014 & 34.8 & 32.8 & 0 & 34.2\% & 1.53 & 0.09 \\
+KNN (T+E) & 724${}_{-30,+42}$ & 0.000 & 36.5 & 31.4 & 0 & 45.4\% & 3.13 & 0.16 \\
+KNN (T) & 651${}_{-34,+35}$ & 0.000 & 37.7 & 28.1 & 0 & 47.1\% & 3.13 & 0.03 \\
+KNN (D) & 462${}_{-59,+47}$ & 0.000 & 39.8 & 39.4 & 0 & 54.4\% & 0.05 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/lite/full/tabarena_leaderboard.csv b/data/lite/full/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..371d69e0973c4710e9432400b4a80989bddba4e1
--- /dev/null
+++ b/data/lite/full/tabarena_leaderboard.csv
@@ -0,0 +1,42 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+AutoGluon 1.3 (4h),8355.364120885437,32.82596967734543,2898.240203433117,4.500812758268476,0.39210856520445686,0.3302537893793854,0.05262855529794473,0.034206828128183674,41965.2048341603,616.5290381859439,6.284313725490196,0.20731,7964.962425470352,4.336968660354614,1453.2679445956664,3.1473262800156863,0.33725328327072535,0.2650602409638539,0.023462982365024088,0.012475884365151627,27194.492186888157,175.7696866507318,5.0,10,8,4,29,1582.5,35.5,28.9,0.8678921568627451,0.37611282305234645
+REALMLP (tuned + ensemble),73262.05650625509,15.495973965700935,6221.197059136006,7.773886071139863,0.5008360344149994,0.46824768418838447,0.07940034886892017,0.054136007823604344,176934.089463731,288.3346092703114,9.431372549019608,0.20743,24379.27822804451,5.209537744522095,6026.466890881138,3.134808014069114,0.48215030896347355,0.4879313729393421,0.03337765485241673,0.021116734936897676,123158.45385063179,235.70660025917869,9.0,2,3,5,41,1479.4,26.5,33.2,0.7892156862745098,0.19658770498159192
+GBM (tuned + ensemble),3051.8140357896395,23.19597311113395,765.6312795719994,3.893554958066526,0.5633713731527658,0.5405886174512831,0.09276981388879593,0.06907753671930644,11175.64311228686,518.7842315699711,10.156862745098039,0.2094,1670.2684843540192,3.933397054672241,416.5640684155986,2.2357814013957977,0.5860060403960241,0.5529709435194232,0.05121532164333675,0.02690185765083355,9062.280137688022,129.84563236620332,10.5,0,0,6,45,1457.3,29.1,26.5,0.7710784313725491,0.13743547238356926
+TABM (tuned + ensemble),36745.76943855192,8.411393100140142,5149.522818881001,3.299204733305397,0.5087786520610308,0.4909227993621131,0.07731815980678898,0.07456986260613699,79103.63593427226,131.94770411319155,10.470588235294118,0.20702,8818.62617468834,3.054008960723877,3133.9115732644937,1.2727714166408632,0.47020083976979304,0.5017082979395318,0.03947783977260777,0.02626206444228642,49987.707956643724,112.26280083003438,8.0,4,2,6,39,1449.2,23.6,33.3,0.763235294117647,0.21701138349898094
+CAT (tuned + ensemble),19506.186602929058,3.204344211840162,4009.454268521621,1.0312463789689519,0.580901607522528,0.5397008238063394,0.08446793583063153,0.05985441446823578,53079.26276755286,76.04544084479011,11.470588235294118,0.20994,7158.277770042419,1.5355591773986816,1665.5315644683697,0.5594151152585728,0.5700946601892775,0.4887362232633289,0.04436795044877406,0.03206777169603318,22918.764355268395,47.71552750484114,10.0,0,1,7,43,1422.4,29.3,24.2,0.7382352941176471,0.1395912997606414
+MNCA (tuned + ensemble),50606.46933756155,274.8289722134085,6200.95174646742,33.492227920869496,0.568211153208397,0.500598405650223,0.087197295730926,0.07544599505597414,106030.16699977385,1982.0660894780217,12.07843137254902,0.2069,14787.927460432053,13.568246603012085,4618.502341732761,7.737494511886756,0.5565369335340675,0.42525853964274546,0.028958734104604367,0.01997226346851291,92410.46095689958,607.7040280210158,9.0,6,4,4,37,1405.8,28.6,32.0,0.7230392156862745,0.2565106522328091
+XGB (tuned + ensemble),5938.998393348619,7.841493718764362,1213.0777679819278,3.1002279660820715,0.6509288335184475,0.6072657031369854,0.0983470702750016,0.07760111598562064,13936.971832341356,215.6442792684636,12.362745098039216,0.20933,2063.2708826065063,2.946662425994873,700.9607998746845,1.4386570273653,0.6793464009669915,0.6107024069682365,0.04410663224092293,0.036526131727220496,11430.712273277264,98.30128646762262,11.0,2,2,1,46,1398.1,27.5,22.8,0.7159313725490196,0.147424742058385
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+TABM (tuned),36745.76943855192,0.9327322455013499,5149.522818881001,0.3522030333025916,0.5996801665884344,0.5784702074771745,0.08660174882794694,0.08901417029096503,79103.63593427226,14.22083187601248,13.166666666666666,0.20774,8818.62617468834,0.25295114517211914,3133.9115732644937,0.13006170213303378,0.5745991505979262,0.565324316015965,0.04427102570326069,0.03586860769423643,49987.707956643724,9.039602142310635,12.0,2,4,1,44,1380.9,20.3,31.6,0.6958333333333333,0.16665774782829965
+GBM (tuned),3051.8140357896395,3.3178744736839745,765.6312795719994,0.6787985114862476,0.6529668965012456,0.6273401467707052,0.10219421875555401,0.08422696989128986,11175.64311228686,83.45556234970765,13.176470588235293,0.20979,1670.2684843540192,0.7316880226135254,416.5640684155986,0.3811195826851199,0.6262832446301,0.6457292820689768,0.0528253340173378,0.037227060560013035,9062.280137688022,21.854692683376335,12.0,0,1,0,50,1380.6,23.9,19.4,0.6955882352941176,0.10739573015819111
+XGB (tuned),5938.998393348619,1.6265712625840132,1213.0777679819278,0.6984321316309747,0.7007828758839686,0.6411202633019268,0.10053526954461836,0.08174521787840415,13936.971832341356,46.56690686937765,13.46078431372549,0.20927,2063.2708826065063,0.5078930854797363,700.9607998746845,0.21305428930075773,0.7223432295573234,0.651749877408692,0.050694852221570774,0.03966653110949333,11430.712273277264,15.141274536165636,13.0,1,2,0,48,1371.7,27.3,22.3,0.6884803921568627,0.12338399887380234
+CAT (default),186.54889291408014,0.4368261870215921,85.73305725415607,0.1697456044717195,0.643860524899134,0.6395421177166652,0.09823054386877146,0.07062879671333311,425.6698111187737,11.747370317227338,13.549019607843137,0.20726,25.447056531906128,0.4332277774810791,6.700347814449044,0.08827101352602937,0.6746502933037238,0.7011272074768353,0.050052492511389324,0.028281581362960058,120.27803545982526,7.036160265837067,13.5,2,0,0,49,1370.3,28.8,32.0,0.6862745098039216,0.12443112696564412
+MNCA (tuned),50606.46933756155,14.969161491768032,6200.95174646742,1.6696617082037302,0.7032607657750378,0.6010415266062749,0.0943412932745321,0.08735862037826053,106030.16699977385,104.17477306753874,13.911764705882353,0.20447,14787.927460432053,0.6251811981201172,4618.502341732761,0.46952933073043823,0.7724401098237351,0.5534523234870296,0.04798611670957431,0.039621112495213905,92410.46095689958,31.933384765725453,13.0,2,3,4,42,1363.3,21.0,26.2,0.6772058823529412,0.16498142344866515
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+NN_TORCH (tuned + ensemble),29357.971773493522,13.246924213334626,3694.4132392234865,3.45030206949641,0.7913423906704912,0.7848484091526847,0.11464529256213314,0.0944932666061636,94556.98181855923,212.38450473432735,17.647058823529413,0.21375,10620.000384569168,3.6842262744903564,2832.7960851387297,1.800873875617981,0.9196144626274988,0.8952879581151866,0.06973898858075078,0.061555288264305126,57294.450258986595,164.18272158737435,18.0,1,0,1,49,1277.4,25.5,25.2,0.5838235294117647,0.09392436864717116
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+XT (tuned + ensemble),1293.0170739772273,3.137296905704573,465.97356682221573,1.4220614441065493,0.8827760158511792,0.8220010106411182,0.15003532717872833,0.12832700332494215,5360.97691412289,92.16814260531523,20.666666666666668,0.21292,775.4602122306824,2.0695900917053223,191.43562446750065,0.7604422990939085,1.0,1.0,0.10450618878352169,0.06621678695786776,3382.254534218774,83.98689534392173,23.5,0,1,1,49,1208.6,23.2,26.7,0.5083333333333333,0.08408535366388699
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+NN_TORCH (tuned),29357.971773493522,0.60710202478895,3694.4132392234865,0.18506171262100615,0.8968088714508368,0.8243014181152737,0.13401199749800358,0.11692743837018824,94556.98181855923,10.572861046995973,21.03921568627451,0.21109,10620.000384569168,0.22349023818969727,2832.7960851387297,0.11152923107147217,1.0,1.0,0.09275938505548831,0.0645493473133498,57294.450258986595,8.23840338809871,22.0,0,0,0,51,1200.8,24.8,24.3,0.49901960784313726,0.0640386668776797
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+XT (tuned),1293.0170739772273,0.34508594344643984,465.97356682221573,0.17473237760756105,0.9078855110953847,0.8260312207745636,0.1592405326093513,0.13428456008408696,5360.97691412289,10.510664591668581,22.08823529411765,0.21281,775.4602122306824,0.17961668968200684,191.43562446750065,0.10098353169828128,1.0,1.0,0.09380491800248536,0.08021624259716303,3382.254534218774,9.053845119655822,26.0,0,3,2,46,1178.1,21.3,23.8,0.4727941176470588,0.09438366419577518
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+NN_TORCH (default),73.82550601865731,0.5703827493331012,14.544520367121898,0.24243997995483998,0.9806438127346997,0.9595306239056958,0.19082179548446107,0.1747856758204516,302.60238317827014,12.920116170611008,28.186274509803923,0.21872,30.76021695137024,0.2736239433288574,8.95763915486452,0.12885630130767822,1.0,1.0,0.12094953272557762,0.11098259528530495,159.50005812712556,8.385666110468058,29.0,0,0,0,51,1029.3,26.0,26.1,0.320343137254902,0.03990968700701428
+RF (default),5.493931228039312,0.18195290658988206,0.9395711378622816,0.07441601332167096,0.9921615843984544,0.9757632253928593,0.21950789037203453,0.22433371107540817,12.413845548911803,5.3715309875205834,29.19607843137255,0.23701,1.763962745666504,0.09250092506408691,0.4316611380184793,0.05251745318464858,1.0,1.0,0.16975626521036435,0.14994321754718537,7.440479734614894,4.454035040702154,31.0,0,0,1,50,1000.0,0.0,0.0,0.2950980392156863,0.041602392383395734
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+XT (default),3.237064623365215,0.20349772771199545,0.7705107029297205,0.07802443108906316,0.976846527522819,0.960990770880773,0.23896991919713237,0.2466584585336881,6.714510413684847,5.481544853676871,30.19607843137255,0.24149,0.9253263473510742,0.08609175682067871,0.25976606260770435,0.0542029349444295,1.0,1.0,0.17303432896286253,0.14892207194946744,5.482206453649765,4.486837961877324,34.0,0,0,0,51,970.8,27.3,27.4,0.27009803921568626,0.03820248213176148
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+LR (default),7.020498261732214,0.4395605956806856,2.5089988631155826,0.17274864462745904,0.9855950536843827,0.9895940411825562,0.3421563043618267,0.42987229655143505,32.25963024407083,8.287063835475498,34.81372549019608,0.25334,5.345212697982788,0.13631606101989746,1.534023110424607,0.08993275118189932,1.0,1.0,0.2768258727778956,0.31807101339614474,17.290213667932342,5.167879066497201,37.0,0,0,0,51,810.4,28.3,31.0,0.15465686274509804,0.030479041022879558
+KNN (tuned + ensemble),191.27347564697266,6.6349917159361,29.50398939167977,0.5506054195292707,1.0,0.987210683055718,0.4540324837867108,0.6121364924236414,81.3768199133465,45.17733544160728,36.509803921568626,0.34601,13.195080518722534,0.23362517356872559,3.133583813905716,0.15550157631257452,1.0,1.0,0.4085005377667177,0.6874431886605319,62.764963980484396,12.85103075181078,39.0,0,0,0,51,723.5,41.5,30.0,0.11225490196078432,0.031857808234694314
+KNN (tuned),191.27347564697266,1.469562432345222,29.50398939167977,0.11124081145987941,1.0,0.9885254568237573,0.47147883710225513,0.6579092058528593,81.3768199133465,9.204095260134709,37.69607843137255,0.34503,13.195080518722534,0.07799005508422852,3.133583813905716,0.030069828033447266,1.0,1.0,0.456326948689309,0.7494456544265635,62.764963980484396,2.2192320534223704,40.0,0,1,0,50,650.8,34.4,33.8,0.08259803921568627,0.03557390516175632
+KNN (default),1.8986088995840036,0.1759517753825468,0.5259164666571766,0.03511341794167653,1.0,1.0,0.5437359502735426,0.9177342094214581,1.049764217774364,2.1650106683540193,39.794117647058826,0.42464,0.2273859977722168,0.03137516975402832,0.0509044812778989,0.01926569938659668,1.0,1.0,0.5148544563369014,1.0,1.0,1.4495271718572689,41.0,0,0,0,51,462.3,46.1,58.9,0.030147058823529412,0.02537743831589345
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diff --git a/data/lite/tabicl-cls/leaderboard.tex b/data/lite/tabicl-cls/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..cfdf59ad945f6644a84f41b36416d63959828e82
--- /dev/null
+++ b/data/lite/tabicl-cls/leaderboard.tex
@@ -0,0 +1,50 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+AutoGluon 1.3 (4h) & \textcolor{gold}{\textbf{1544${}_{-34,+36}$}} & \textcolor{gold}{\textbf{0.569}} & \textcolor{gold}{\textbf{7.4}} & \textcolor{gold}{\textbf{2.9}} & \textcolor{gold}{\textbf{7}} & \textcolor{gold}{\textbf{6.9\%}} & 1239.48 & 1.98 \\
+TabM (T+E) & \textcolor{silver}{\textbf{1445${}_{-33,+31}$}} & \textcolor{bronze}{\textbf{0.491}} & \textcolor{silver}{\textbf{10.9}} & \textcolor{bronze}{\textbf{4.4}} & \textcolor{bronze}{\textbf{3}} & \textcolor{bronze}{\textbf{9.8\%}} & 2261.57 & 1.26 \\
+RealMLP (T+E) & \textcolor{bronze}{\textbf{1441${}_{-37,+31}$}} & 0.418 & \textcolor{bronze}{\textbf{11.0}} & 6.6 & 0 & 10.5\% & 5429.82 & 2.94 \\
+LightGBM (T+E) & 1420${}_{-29,+29}$ & 0.389 & 11.8 & 8.6 & 0 & 11.6\% & 368.47 & 1.22 \\
+TabICL (D) & 1407${}_{-35,+24}$ & \textcolor{silver}{\textbf{0.521}} & 12.4 & \textcolor{silver}{\textbf{4.2}} & \textcolor{silver}{\textbf{5}} & \textcolor{silver}{\textbf{9.3\%}} & 9.07 & 2.11 \\
+CatBoost (T+E) & 1403${}_{-33,+28}$ & 0.377 & 12.6 & 7.3 & 0 & 10.5\% & 1207.49 & 0.52 \\
+XGBoost (T+E) & 1395${}_{-22,+30}$ & 0.320 & 12.7 & 6.8 & 1 & 12.1\% & 655.54 & 0.78 \\
+ModernNCA (T) & 1385${}_{-31,+30}$ & 0.275 & 13.3 & 6.2 & 2 & 10.7\% & 4498.66 & 0.45 \\
+CatBoost (T) & 1375${}_{-26,+30}$ & 0.360 & 13.7 & 9.0 & 0 & 11.2\% & 1207.49 & 0.05 \\
+ModernNCA (T+E) & 1375${}_{-33,+32}$ & 0.383 & 13.6 & 5.2 & 2 & 10.8\% & 4498.66 & 8.48 \\
+TabM (T) & 1373${}_{-32,+31}$ & 0.409 & 13.7 & 5.5 & 2 & 10.8\% & 2261.57 & 0.13 \\
+CatBoost (D) & 1372${}_{-28,+27}$ & 0.343 & 13.8 & 7.4 & 2 & 11.6\% & 5.18 & 0.09 \\
+XGBoost (T) & 1359${}_{-27,+27}$ & 0.264 & 14.3 & 8.1 & 1 & 12.4\% & 655.54 & 0.16 \\
+LightGBM (T) & 1355${}_{-31,+27}$ & 0.302 & 14.6 & 9.8 & 0 & 12.6\% & 368.47 & 0.12 \\
+TabM (D) & 1308${}_{-24,+27}$ & 0.272 & 16.7 & 7.6 & 1 & 14.3\% & 9.30 & 0.13 \\
+RealMLP (T) & 1289${}_{-28,+35}$ & 0.198 & 17.5 & 11.4 & 0 & 14.3\% & 5429.82 & 0.17 \\
+EBM (T+E) & 1289${}_{-37,+29}$ & 0.170 & 17.5 & 7.2 & 2 & 16.1\% & 852.29 & 0.21 \\
+TorchMLP (T+E) & 1283${}_{-34,+27}$ & 0.228 & 17.7 & 11.2 & 0 & 13.5\% & 2406.10 & 2.04 \\
+EBM (T) & 1242${}_{-28,+33}$ & 0.110 & 19.7 & 10.4 & 0 & 16.8\% & 852.29 & 0.02 \\
+FastaiMLP (T+E) & 1224${}_{-32,+25}$ & 0.207 & 20.7 & 11.5 & 0 & 17.6\% & 581.37 & 4.78 \\
+EBM (D) & 1211${}_{-32,+27}$ & 0.119 & 21.3 & 10.0 & 1 & 17.6\% & 4.40 & 0.05 \\
+TorchMLP (T) & 1199${}_{-30,+29}$ & 0.085 & 21.9 & 16.9 & 0 & 15.9\% & 2406.10 & 0.11 \\
+XGBoost (D) & 1198${}_{-26,+31}$ & 0.102 & 21.7 & 12.0 & 1 & 15.7\% & 1.66 & 0.12 \\
+RealMLP (D) & 1197${}_{-28,+35}$ & 0.095 & 21.8 & 11.7 & 1 & 15.9\% & 34.62 & 0.17 \\
+ModernNCA (D) & 1192${}_{-27,+28}$ & 0.142 & 22.1 & 11.1 & 1 & 17.3\% & 12.99 & 0.34 \\
+ExtraTrees (T+E) & 1182${}_{-28,+26}$ & 0.082 & 22.6 & 15.7 & 0 & 17.7\% & 187.87 & 0.72 \\
+RandomForest (T+E) & 1155${}_{-25,+37}$ & 0.067 & 23.8 & 11.0 & 1 & 18.0\% & 266.25 & 0.64 \\
+TabDPT (D) & 1148${}_{-35,+28}$ & 0.186 & 24.3 & 7.6 & 2 & 18.6\% & 20.26 & 8.55 \\
+ExtraTrees (T) & 1146${}_{-34,+33}$ & 0.056 & 24.3 & 12.6 & 0 & 18.8\% & 187.87 & 0.08 \\
+FastaiMLP (T) & 1142${}_{-36,+29}$ & 0.094 & 24.5 & 15.3 & 0 & 19.7\% & 581.37 & 0.33 \\
+LightGBM (D) & 1135${}_{-33,+24}$ & 0.053 & 24.8 & 21.5 & 0 & 17.6\% & 1.64 & 0.15 \\
+RandomForest (T) & 1110${}_{-28,+35}$ & 0.045 & 25.8 & 17.3 & 0 & 18.8\% & 266.25 & 0.07 \\
+TorchMLP (D) & 1026${}_{-34,+29}$ & 0.022 & 29.3 & 25.6 & 0 & 22.2\% & 5.44 & 0.14 \\
+FastaiMLP (D) & 1005${}_{-34,+29}$ & 0.027 & 30.1 & 26.3 & 0 & 23.6\% & 3.04 & 0.30 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.003 & 30.3 & 23.4 & 0 & 25.3\% & 0.35 & 0.03 \\
+Linear (T+E) & 946${}_{-30,+33}$ & 0.044 & 32.2 & 17.1 & 1 & 30.4\% & 44.56 & 0.16 \\
+ExtraTrees (D) & 928${}_{-33,+33}$ & 0.011 & 32.8 & 28.7 & 0 & 28.3\% & 0.24 & 0.04 \\
+Linear (T) & 912${}_{-27,+36}$ & 0.028 & 33.2 & 25.8 & 0 & 31.1\% & 44.56 & 0.06 \\
+Linear (D) & 888${}_{-34,+26}$ & 0.020 & 34.0 & 31.7 & 0 & 32.1\% & 1.42 & 0.09 \\
+KNN (T+E) & 748${}_{-36,+40}$ & 0.000 & 37.3 & 30.3 & 0 & 49.6\% & 3.50 & 0.16 \\
+KNN (T) & 676${}_{-43,+42}$ & 0.000 & 38.5 & 25.8 & 0 & 51.7\% & 3.50 & 0.03 \\
+KNN (D) & 457${}_{-75,+55}$ & 0.000 & 40.9 & 40.5 & 0 & 60.2\% & 0.07 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/lite/tabicl-cls/tabarena_leaderboard.csv b/data/lite/tabicl-cls/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a1569fad5517e62455bb72ecc7789a0de91e3da1
--- /dev/null
+++ b/data/lite/tabicl-cls/tabarena_leaderboard.csv
@@ -0,0 +1,43 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
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+TABM (tuned + ensemble),32822.329592857095,6.718376735846202,3197.494145595591,1.5968882956786812,0.5092283925039975,0.507000043838314,0.0984603164003323,0.08012828297402202,47368.16918956869,126.38148088436719,10.916666666666666,0.179425,8087.822376012802,2.9963064193725586,2261.5736774780025,1.258481528676095,0.47136645123500964,0.5108291907783599,0.060615421067887476,0.023331148793904504,38436.80992013264,113.73588992793104,8.5,3,2,4,27,1444.7,30.6,32.5,0.758130081300813,0.22495824385743393
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+GBM (tuned + ensemble),2716.464209013515,11.650436507331001,652.7081231589766,2.3316901429136103,0.6113071815088934,0.6115648021204206,0.11626709694511878,0.0824232864688556,8680.115930381902,215.8804964577198,11.75,0.17347,1527.4595284461975,3.0867438316345215,368.4672845277482,1.2233637093106904,0.6534872634143774,0.6466968889868911,0.07038437664020675,0.02975866915001093,6991.1315945904835,101.20971107477635,12.0,0,0,3,33,1420.3,28.8,28.8,0.7378048780487805,0.11670989654152485
+TABICL (default),122.91359008020825,22.535752693812054,9.963658601166212,2.414261101064252,0.4788051732855598,0.5443303646525952,0.09266040289556793,0.06762151146287841,179.76964423723584,285.61149302454663,12.402777777777779,0.17301,25.354220509529114,3.4422082901000977,9.073519792448254,2.1146179912287675,0.4992564187967412,0.5686403350371668,0.050317184697893425,0.021088283054317383,144.31099154558206,133.6960355402875,12.25,5,2,1,28,1406.6,23.5,34.5,0.7218834688346883,0.24049138766638822
+CAT (tuned + ensemble),14495.012520273527,2.4179346097840204,2435.7734425933513,0.7431584046715475,0.6226459246322733,0.5724409794042533,0.10496616447275113,0.0672300454324769,26875.894999800756,51.46768059921607,12.583333333333334,0.16073500000000002,4554.886538267136,1.2364250421524048,1207.4876040106037,0.5178688292431316,0.6153632608570143,0.555056424681614,0.05369915486940119,0.03126669058324277,20500.32079395515,42.031537823549,11.75,0,1,4,31,1402.6,27.7,32.6,0.717479674796748,0.13630557293973747
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diff --git a/data/lite/tabicl-cls/tuning-impact-elo-horizontal.pdf b/data/lite/tabicl-cls/tuning-impact-elo-horizontal.pdf
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diff --git a/data/lite/tabicl/leaderboard.tex b/data/lite/tabicl/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..cfdf59ad945f6644a84f41b36416d63959828e82
--- /dev/null
+++ b/data/lite/tabicl/leaderboard.tex
@@ -0,0 +1,50 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+AutoGluon 1.3 (4h) & \textcolor{gold}{\textbf{1544${}_{-34,+36}$}} & \textcolor{gold}{\textbf{0.569}} & \textcolor{gold}{\textbf{7.4}} & \textcolor{gold}{\textbf{2.9}} & \textcolor{gold}{\textbf{7}} & \textcolor{gold}{\textbf{6.9\%}} & 1239.48 & 1.98 \\
+TabM (T+E) & \textcolor{silver}{\textbf{1445${}_{-33,+31}$}} & \textcolor{bronze}{\textbf{0.491}} & \textcolor{silver}{\textbf{10.9}} & \textcolor{bronze}{\textbf{4.4}} & \textcolor{bronze}{\textbf{3}} & \textcolor{bronze}{\textbf{9.8\%}} & 2261.57 & 1.26 \\
+RealMLP (T+E) & \textcolor{bronze}{\textbf{1441${}_{-37,+31}$}} & 0.418 & \textcolor{bronze}{\textbf{11.0}} & 6.6 & 0 & 10.5\% & 5429.82 & 2.94 \\
+LightGBM (T+E) & 1420${}_{-29,+29}$ & 0.389 & 11.8 & 8.6 & 0 & 11.6\% & 368.47 & 1.22 \\
+TabICL (D) & 1407${}_{-35,+24}$ & \textcolor{silver}{\textbf{0.521}} & 12.4 & \textcolor{silver}{\textbf{4.2}} & \textcolor{silver}{\textbf{5}} & \textcolor{silver}{\textbf{9.3\%}} & 9.07 & 2.11 \\
+CatBoost (T+E) & 1403${}_{-33,+28}$ & 0.377 & 12.6 & 7.3 & 0 & 10.5\% & 1207.49 & 0.52 \\
+XGBoost (T+E) & 1395${}_{-22,+30}$ & 0.320 & 12.7 & 6.8 & 1 & 12.1\% & 655.54 & 0.78 \\
+ModernNCA (T) & 1385${}_{-31,+30}$ & 0.275 & 13.3 & 6.2 & 2 & 10.7\% & 4498.66 & 0.45 \\
+CatBoost (T) & 1375${}_{-26,+30}$ & 0.360 & 13.7 & 9.0 & 0 & 11.2\% & 1207.49 & 0.05 \\
+ModernNCA (T+E) & 1375${}_{-33,+32}$ & 0.383 & 13.6 & 5.2 & 2 & 10.8\% & 4498.66 & 8.48 \\
+TabM (T) & 1373${}_{-32,+31}$ & 0.409 & 13.7 & 5.5 & 2 & 10.8\% & 2261.57 & 0.13 \\
+CatBoost (D) & 1372${}_{-28,+27}$ & 0.343 & 13.8 & 7.4 & 2 & 11.6\% & 5.18 & 0.09 \\
+XGBoost (T) & 1359${}_{-27,+27}$ & 0.264 & 14.3 & 8.1 & 1 & 12.4\% & 655.54 & 0.16 \\
+LightGBM (T) & 1355${}_{-31,+27}$ & 0.302 & 14.6 & 9.8 & 0 & 12.6\% & 368.47 & 0.12 \\
+TabM (D) & 1308${}_{-24,+27}$ & 0.272 & 16.7 & 7.6 & 1 & 14.3\% & 9.30 & 0.13 \\
+RealMLP (T) & 1289${}_{-28,+35}$ & 0.198 & 17.5 & 11.4 & 0 & 14.3\% & 5429.82 & 0.17 \\
+EBM (T+E) & 1289${}_{-37,+29}$ & 0.170 & 17.5 & 7.2 & 2 & 16.1\% & 852.29 & 0.21 \\
+TorchMLP (T+E) & 1283${}_{-34,+27}$ & 0.228 & 17.7 & 11.2 & 0 & 13.5\% & 2406.10 & 2.04 \\
+EBM (T) & 1242${}_{-28,+33}$ & 0.110 & 19.7 & 10.4 & 0 & 16.8\% & 852.29 & 0.02 \\
+FastaiMLP (T+E) & 1224${}_{-32,+25}$ & 0.207 & 20.7 & 11.5 & 0 & 17.6\% & 581.37 & 4.78 \\
+EBM (D) & 1211${}_{-32,+27}$ & 0.119 & 21.3 & 10.0 & 1 & 17.6\% & 4.40 & 0.05 \\
+TorchMLP (T) & 1199${}_{-30,+29}$ & 0.085 & 21.9 & 16.9 & 0 & 15.9\% & 2406.10 & 0.11 \\
+XGBoost (D) & 1198${}_{-26,+31}$ & 0.102 & 21.7 & 12.0 & 1 & 15.7\% & 1.66 & 0.12 \\
+RealMLP (D) & 1197${}_{-28,+35}$ & 0.095 & 21.8 & 11.7 & 1 & 15.9\% & 34.62 & 0.17 \\
+ModernNCA (D) & 1192${}_{-27,+28}$ & 0.142 & 22.1 & 11.1 & 1 & 17.3\% & 12.99 & 0.34 \\
+ExtraTrees (T+E) & 1182${}_{-28,+26}$ & 0.082 & 22.6 & 15.7 & 0 & 17.7\% & 187.87 & 0.72 \\
+RandomForest (T+E) & 1155${}_{-25,+37}$ & 0.067 & 23.8 & 11.0 & 1 & 18.0\% & 266.25 & 0.64 \\
+TabDPT (D) & 1148${}_{-35,+28}$ & 0.186 & 24.3 & 7.6 & 2 & 18.6\% & 20.26 & 8.55 \\
+ExtraTrees (T) & 1146${}_{-34,+33}$ & 0.056 & 24.3 & 12.6 & 0 & 18.8\% & 187.87 & 0.08 \\
+FastaiMLP (T) & 1142${}_{-36,+29}$ & 0.094 & 24.5 & 15.3 & 0 & 19.7\% & 581.37 & 0.33 \\
+LightGBM (D) & 1135${}_{-33,+24}$ & 0.053 & 24.8 & 21.5 & 0 & 17.6\% & 1.64 & 0.15 \\
+RandomForest (T) & 1110${}_{-28,+35}$ & 0.045 & 25.8 & 17.3 & 0 & 18.8\% & 266.25 & 0.07 \\
+TorchMLP (D) & 1026${}_{-34,+29}$ & 0.022 & 29.3 & 25.6 & 0 & 22.2\% & 5.44 & 0.14 \\
+FastaiMLP (D) & 1005${}_{-34,+29}$ & 0.027 & 30.1 & 26.3 & 0 & 23.6\% & 3.04 & 0.30 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.003 & 30.3 & 23.4 & 0 & 25.3\% & 0.35 & 0.03 \\
+Linear (T+E) & 946${}_{-30,+33}$ & 0.044 & 32.2 & 17.1 & 1 & 30.4\% & 44.56 & 0.16 \\
+ExtraTrees (D) & 928${}_{-33,+33}$ & 0.011 & 32.8 & 28.7 & 0 & 28.3\% & 0.24 & 0.04 \\
+Linear (T) & 912${}_{-27,+36}$ & 0.028 & 33.2 & 25.8 & 0 & 31.1\% & 44.56 & 0.06 \\
+Linear (D) & 888${}_{-34,+26}$ & 0.020 & 34.0 & 31.7 & 0 & 32.1\% & 1.42 & 0.09 \\
+KNN (T+E) & 748${}_{-36,+40}$ & 0.000 & 37.3 & 30.3 & 0 & 49.6\% & 3.50 & 0.16 \\
+KNN (T) & 676${}_{-43,+42}$ & 0.000 & 38.5 & 25.8 & 0 & 51.7\% & 3.50 & 0.03 \\
+KNN (D) & 457${}_{-75,+55}$ & 0.000 & 40.9 & 40.5 & 0 & 60.2\% & 0.07 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/lite/tabicl/tabarena_leaderboard.csv b/data/lite/tabicl/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a1569fad5517e62455bb72ecc7789a0de91e3da1
--- /dev/null
+++ b/data/lite/tabicl/tabarena_leaderboard.csv
@@ -0,0 +1,43 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
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+TABM (tuned + ensemble),32822.329592857095,6.718376735846202,3197.494145595591,1.5968882956786812,0.5092283925039975,0.507000043838314,0.0984603164003323,0.08012828297402202,47368.16918956869,126.38148088436719,10.916666666666666,0.179425,8087.822376012802,2.9963064193725586,2261.5736774780025,1.258481528676095,0.47136645123500964,0.5108291907783599,0.060615421067887476,0.023331148793904504,38436.80992013264,113.73588992793104,8.5,3,2,4,27,1444.7,30.6,32.5,0.758130081300813,0.22495824385743393
+REALMLP (tuned + ensemble),78236.15068201887,12.508955962128109,5830.1148293797,4.304066546062194,0.5822217846671746,0.540978236726168,0.1054473766976048,0.06442528212733184,142054.31198219568,287.5042196718009,11.0,0.172085,23325.48352110386,5.581948518753052,5429.819541400706,2.9376822787963643,0.5258237901453162,0.524882615107501,0.06227050818890584,0.031582684665151774,93177.39784523446,229.06556046190957,11.0,0,2,3,31,1441.2,30.9,36.7,0.7560975609756098,0.1515892708417218
+GBM (tuned + ensemble),2716.464209013515,11.650436507331001,652.7081231589766,2.3316901429136103,0.6113071815088934,0.6115648021204206,0.11626709694511878,0.0824232864688556,8680.115930381902,215.8804964577198,11.75,0.17347,1527.4595284461975,3.0867438316345215,368.4672845277482,1.2233637093106904,0.6534872634143774,0.6466968889868911,0.07038437664020675,0.02975866915001093,6991.1315945904835,101.20971107477635,12.0,0,0,3,33,1420.3,28.8,28.8,0.7378048780487805,0.11670989654152485
+TABICL (default),122.91359008020825,22.535752693812054,9.963658601166212,2.414261101064252,0.4788051732855598,0.5443303646525952,0.09266040289556793,0.06762151146287841,179.76964423723584,285.61149302454663,12.402777777777779,0.17301,25.354220509529114,3.4422082901000977,9.073519792448254,2.1146179912287675,0.4992564187967412,0.5686403350371668,0.050317184697893425,0.021088283054317383,144.31099154558206,133.6960355402875,12.25,5,2,1,28,1406.6,23.5,34.5,0.7218834688346883,0.24049138766638822
+CAT (tuned + ensemble),14495.012520273527,2.4179346097840204,2435.7734425933513,0.7431584046715475,0.6226459246322733,0.5724409794042533,0.10496616447275113,0.0672300454324769,26875.894999800756,51.46768059921607,12.583333333333334,0.16073500000000002,4554.886538267136,1.2364250421524048,1207.4876040106037,0.5178688292431316,0.6153632608570143,0.555056424681614,0.05369915486940119,0.03126669058324277,20500.32079395515,42.031537823549,11.75,0,1,4,31,1402.6,27.7,32.6,0.717479674796748,0.13630557293973747
+XGB (tuned + ensemble),5342.747825033135,6.078431023491754,903.1395181375539,2.4252994962785444,0.6804014135907539,0.6264870966428019,0.12111620645907134,0.08995137231528924,10629.483194714896,137.278973628425,12.680555555555555,0.17348,1566.1410616636276,2.2440043687820435,655.5367556867432,0.7811829897319023,0.7126419091535116,0.6325401095781271,0.09321543161193863,0.03379706826049186,7509.936420425425,78.99719729544988,10.5,1,2,1,32,1395.2,29.8,21.5,0.7151084010840109,0.14600195845562308
+MNCA (tuned),57242.54890214073,19.57283937268787,5418.777655567625,1.42346301375637,0.7251534847407544,0.5849087413461024,0.10710451817631175,0.08805735190470587,83536.32309061421,128.61468675559033,13.319444444444445,0.17875999999999997,13889.081128954887,0.5763018131256104,4498.662765534045,0.4537708228509102,0.7744612480983963,0.5544791639834478,0.0863080541794865,0.030308265873669055,68177.28399410362,31.783037323749284,11.5,2,0,1,33,1384.6,29.3,30.1,0.6995257452574526,0.1615009946329935
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+CAT (tuned),14495.012520273527,0.51534503698349,2435.7734425933513,0.10399437886071032,0.6397756322608608,0.6322254665313207,0.11211575515013829,0.07212514514540504,26875.894999800756,8.46801242481876,13.708333333333334,0.16238999999999998,4554.886538267136,0.0815126895904541,1207.4876040106037,0.0500282797740022,0.6663747291561838,0.6867488983507399,0.06911800649977579,0.037329065440735124,20500.32079395515,3.2560454821039713,13.0,0,1,1,34,1375.2,29.1,25.7,0.6900406504065041,0.11055045510865025
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+TABM (default),140.8987251520157,0.9393433663580153,14.983795328833262,0.18062205057314412,0.7280741356398281,0.6887171968757086,0.1434354286809727,0.11695380949114573,217.11963453230365,13.588062902891982,16.694444444444443,0.18336999999999998,29.720080256462097,0.17920684814453125,9.303649390343331,0.12625846443862987,0.8404844924976476,0.7247027492327622,0.09137336521284833,0.04657437619358792,136.86363214905396,9.928949591639647,15.0,1,2,1,32,1308.3,26.2,23.4,0.6172086720867209,0.1309363094547008
+REALMLP (tuned),78236.15068201887,0.5794834030999078,5830.1148293797,0.2343189481249011,0.8023310570642167,0.7611882470431272,0.14345077264424028,0.10287611571863375,142054.31198219568,14.164064071067262,17.47222222222222,0.17396499999999998,23325.48352110386,0.2515758275985718,5429.819541400706,0.1667243421007542,0.8787741747777617,0.848557552697969,0.12726966477565288,0.052454140783326594,93177.39784523446,11.047988768711498,17.0,0,1,0,35,1289.1,34.7,27.8,0.5982384823848238,0.08738586997653082
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diff --git a/data/lite/tabicl/tuning-impact-elo-horizontal.pdf b/data/lite/tabicl/tuning-impact-elo-horizontal.pdf
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diff --git a/data/lite/tabpfn-cls/leaderboard.tex b/data/lite/tabpfn-cls/leaderboard.tex
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+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+TabPFNv2 (T+E) & \textcolor{gold}{\textbf{1501${}_{-28,+39}$}} & \textcolor{gold}{\textbf{0.700}} & \textcolor{gold}{\textbf{8.1}} & \textcolor{gold}{\textbf{2.7}} & \textcolor{gold}{\textbf{6}} & \textcolor{gold}{\textbf{7.6\%}} & 3361.32 & 51.67 \\
+AutoGluon 1.3 (4h) & \textcolor{silver}{\textbf{1435${}_{-30,+47}$}} & 0.440 & \textcolor{silver}{\textbf{10.6}} & 6.0 & 1 & \textcolor{silver}{\textbf{10.0\%}} & 2085.73 & 2.16 \\
+RealMLP (T+E) & \textcolor{bronze}{\textbf{1409${}_{-33,+36}$}} & 0.402 & \textcolor{bronze}{\textbf{11.7}} & 6.0 & 1 & 11.8\% & 5575.69 & 4.58 \\
+TabPFNv2 (T) & 1406${}_{-37,+37}$ & \textcolor{silver}{\textbf{0.544}} & 11.8 & \textcolor{silver}{\textbf{4.3}} & 1 & \textcolor{bronze}{\textbf{10.2\%}} & 3361.32 & 0.53 \\
+TabM (T+E) & 1389${}_{-24,+39}$ & 0.483 & 12.4 & 6.0 & 1 & 11.5\% & 2899.02 & 1.46 \\
+LightGBM (T+E) & 1350${}_{-36,+39}$ & 0.319 & 14.2 & 11.2 & 0 & 13.8\% & 691.46 & 1.46 \\
+TabPFNv2 (D) & 1334${}_{-30,+34}$ & \textcolor{bronze}{\textbf{0.499}} & 15.0 & \textcolor{bronze}{\textbf{4.4}} & \textcolor{silver}{\textbf{3}} & 11.6\% & 4.28 & 0.51 \\
+TabM (T) & 1332${}_{-31,+42}$ & 0.387 & 15.2 & 7.5 & 0 & 12.7\% & 2899.02 & 0.17 \\
+CatBoost (T+E) & 1329${}_{-28,+37}$ & 0.271 & 15.3 & 9.5 & 0 & 13.1\% & 1394.45 & 0.62 \\
+ModernNCA (T) & 1326${}_{-27,+40}$ & 0.255 & 15.4 & 6.1 & 2 & 12.2\% & 6020.89 & 0.45 \\
+XGBoost (T+E) & 1314${}_{-28,+29}$ & 0.222 & 16.0 & 11.1 & 0 & 14.9\% & 785.90 & 1.28 \\
+CatBoost (D) & 1312${}_{-40,+34}$ & 0.228 & 16.1 & 9.4 & 1 & 14.4\% & 5.74 & 0.14 \\
+CatBoost (T) & 1304${}_{-36,+31}$ & 0.247 & 16.5 & 12.4 & 0 & 14.0\% & 1394.45 & 0.05 \\
+ModernNCA (T+E) & 1292${}_{-34,+36}$ & 0.307 & 17.1 & 8.4 & 0 & 14.0\% & 6020.89 & 8.48 \\
+LightGBM (T) & 1292${}_{-34,+35}$ & 0.262 & 17.1 & 11.5 & 0 & 14.8\% & 691.46 & 0.26 \\
+XGBoost (T) & 1291${}_{-34,+30}$ & 0.181 & 17.2 & 9.5 & 1 & 15.1\% & 785.90 & 0.21 \\
+TabM (D) & 1283${}_{-34,+36}$ & 0.288 & 17.5 & 7.9 & 1 & 15.9\% & 11.60 & 0.15 \\
+EBM (T+E) & 1278${}_{-32,+29}$ & 0.186 & 17.8 & 7.9 & 0 & 16.8\% & 1142.31 & 0.24 \\
+TorchMLP (T+E) & 1249${}_{-29,+36}$ & 0.199 & 19.2 & 11.6 & 0 & 15.3\% & 2792.14 & 2.18 \\
+RealMLP (T) & 1232${}_{-29,+38}$ & 0.201 & 20.1 & 13.0 & 0 & 16.1\% & 5575.69 & 0.24 \\
+EBM (T) & 1230${}_{-38,+37}$ & 0.120 & 20.2 & 9.0 & 1 & 17.7\% & 1142.31 & 0.03 \\
+EBM (D) & 1206${}_{-33,+38}$ & 0.153 & 21.5 & 9.5 & 1 & 18.4\% & 5.57 & 0.07 \\
+FastaiMLP (T+E) & 1204${}_{-36,+42}$ & 0.230 & 21.6 & 11.9 & 0 & 18.7\% & 1350.04 & 6.57 \\
+ModernNCA (D) & 1194${}_{-31,+33}$ & 0.183 & 22.1 & 9.6 & 1 & 19.0\% & 13.67 & 0.34 \\
+TabDPT (D) & 1183${}_{-43,+34}$ & 0.258 & 22.7 & 5.5 & \textcolor{silver}{\textbf{3}} & 17.9\% & 28.08 & 8.76 \\
+RealMLP (D) & 1176${}_{-28,+43}$ & 0.083 & 22.9 & 10.8 & 1 & 17.1\% & 35.27 & 0.25 \\
+TorchMLP (T) & 1168${}_{-31,+40}$ & 0.086 & 23.3 & 17.2 & 0 & 17.3\% & 2792.14 & 0.13 \\
+ExtraTrees (T+E) & 1156${}_{-36,+31}$ & 0.102 & 24.1 & 15.4 & 0 & 20.0\% & 378.34 & 1.37 \\
+FastaiMLP (T) & 1140${}_{-33,+34}$ & 0.113 & 24.7 & 13.2 & 0 & 20.2\% & 1350.04 & 0.61 \\
+XGBoost (D) & 1134${}_{-32,+40}$ & 0.032 & 25.2 & 21.1 & 0 & 18.8\% & 2.66 & 0.17 \\
+ExtraTrees (T) & 1131${}_{-25,+36}$ & 0.072 & 25.2 & 13.4 & 0 & 20.3\% & 378.34 & 0.15 \\
+RandomForest (T+E) & 1111${}_{-35,+36}$ & 0.057 & 26.2 & 14.1 & 0 & 20.7\% & 527.27 & 1.16 \\
+LightGBM (D) & 1104${}_{-32,+34}$ & 0.030 & 26.7 & 23.2 & 0 & 19.8\% & 3.01 & 0.18 \\
+RandomForest (T) & 1068${}_{-38,+34}$ & 0.023 & 28.3 & 21.6 & 0 & 21.6\% & 527.27 & 0.13 \\
+TorchMLP (D) & 1010${}_{-28,+33}$ & 0.030 & 30.8 & 25.8 & 0 & 24.4\% & 8.93 & 0.17 \\
+FastaiMLP (D) & 1001${}_{-39,+36}$ & 0.019 & 31.2 & 26.7 & 0 & 24.2\% & 4.68 & 0.55 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.005 & 31.3 & 24.0 & 0 & 27.1\% & 0.44 & 0.06 \\
+Linear (T+E) & 948${}_{-34,+36}$ & 0.055 & 33.2 & 14.8 & 1 & 31.1\% & 89.12 & 0.20 \\
+ExtraTrees (D) & 948${}_{-34,+43}$ & 0.015 & 33.2 & 28.2 & 0 & 29.0\% & 0.36 & 0.07 \\
+Linear (T) & 928${}_{-39,+36}$ & 0.035 & 34.0 & 24.2 & 0 & 31.7\% & 89.12 & 0.08 \\
+Linear (D) & 894${}_{-36,+44}$ & 0.025 & 35.0 & 32.1 & 0 & 33.1\% & 2.43 & 0.10 \\
+KNN (T+E) & 747${}_{-45,+40}$ & 0.000 & 39.0 & 32.4 & 0 & 47.5\% & 3.50 & 0.16 \\
+KNN (T) & 672${}_{-50,+46}$ & 0.000 & 40.2 & 27.8 & 0 & 50.0\% & 3.50 & 0.03 \\
+KNN (D) & 380${}_{-82,+54}$ & 0.000 & 43.3 & 43.2 & 0 & 59.3\% & 0.07 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/lite/tabpfn-cls/tabarena_leaderboard.csv b/data/lite/tabpfn-cls/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..92a212f0633c2f81c84f668f29903ffa52671362
--- /dev/null
+++ b/data/lite/tabpfn-cls/tabarena_leaderboard.csv
@@ -0,0 +1,45 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+TABPFNV2 (tuned + ensemble),14624.10810300937,166.6936253951146,3837.093570770289,71.22922446033486,0.2999799624009034,0.36808629417652067,0.07554916766810474,0.0510069061361108,73712.17169915051,6326.444952096131,8.115384615384615,0.1749,8447.90291428566,59.9476752281189,3361.317280362242,51.668880171806535,0.21441671773452015,0.3610327130222525,0.03548854933740464,0.027745461982937078,32774.70384527536,4094.471326370238,5.5,6,3,1,16,1501.4,38.8,27.3,0.8345259391771019,0.37210235277542975
+AutoGluon 1.3 (4h),5811.540137483524,3.0737958137805643,3715.2599271484114,3.044362316385498,0.5599854226187498,0.5374717793951389,0.09973935537830267,0.056003751651180655,37983.87924037632,165.48830983683717,10.557692307692308,0.17751,5213.819254875183,2.0895845890045166,2085.7270446537464,2.161349128608518,0.5600808158121042,0.5370720772033579,0.06078825021132733,0.052199978656074855,30345.10100676535,118.3021811054546,8.5,1,1,0,24,1434.9,46.6,29.2,0.7777280858676208,0.1677722095709093
+REALMLP (tuned + ensemble),17853.666671056013,5.060677555891184,6069.468633023759,5.352972929535142,0.5983187605703805,0.5442978650575347,0.1181226678591906,0.06571886126067156,114488.59335090281,312.9299756303972,11.653846153846153,0.180765,10652.850141763687,4.7910391092300415,5575.693006637695,4.577857176513236,0.5696681015084324,0.5160105348256356,0.06341551053522204,0.040531971105630976,88499.22644289241,286.2007603461078,11.0,1,0,2,23,1409.1,35.1,32.7,0.7522361359570662,0.16703892033583626
+TABPFNV2 (tuned),14624.10810300937,5.890562671881455,3837.093570770289,2.9216612176257093,0.4562783530638654,0.5323385828743338,0.10174827499736666,0.07249836526386379,73712.17169915051,208.74723417223507,11.75,0.19132,8447.90291428566,0.5261704921722412,3361.317280362242,0.52729181819818,0.34109768962419973,0.5750212986748566,0.08017059655741715,0.04680159037032619,32774.70384527536,36.401677545743524,10.5,1,6,2,17,1406.3,36.8,36.9,0.75,0.2346239346014619
+TABM (tuned + ensemble),7183.65333878994,2.169150572556716,3570.712955098323,1.861754182648195,0.5166525238887076,0.5375782342693337,0.11533720579631103,0.07923336429395993,42989.483218042995,121.47528176372664,12.384615384615385,0.18310500000000002,6058.230099320412,1.6269241571426392,2899.0220559668596,1.460880880345262,0.47136645123500964,0.5091829772196896,0.060569889934732224,0.040297742432766875,37327.19260107514,109.27107326295308,9.0,1,0,2,23,1388.6,38.5,23.6,0.7352415026833632,0.16758336662017792
+GBM (tuned + ensemble),1386.6633568268555,2.647538212629465,855.5354588276103,2.6861264004396594,0.6806482110513491,0.6915952965141915,0.1377345967845975,0.08426211130205394,10078.423717398238,182.57724180654643,14.25,0.18084,1317.1929507255554,2.008246660232544,691.4641967411458,1.4553717527055694,0.7005535661954161,0.7687759906277638,0.07096598380907965,0.06332006129013433,9059.644744264673,90.72358893879789,14.0,0,0,1,25,1350.4,38.4,35.8,0.6918604651162791,0.08898620150852825
+TABPFNV2 (default),12.006092300781837,1.2122490681134737,5.3148066442099084,0.6434373636958165,0.5013642769155608,0.5985164446892546,0.11636281251306395,0.08655794673306556,74.57934257049592,50.66248833495304,15.0,0.194575,9.273962259292603,0.4255213737487793,4.283948847917442,0.5110342254172261,0.3771347187714357,0.749999609789962,0.07359963710195983,0.07175881685986511,62.366726819804335,28.80821414198816,13.5,3,2,2,19,1334.4,33.9,29.5,0.6744186046511628,0.2263587639687604
+TABM (tuned),7183.65333878994,0.2019361532651461,3570.712955098323,0.2168923191387062,0.6127855914191946,0.6015979357053903,0.12668145442804887,0.09163884859097308,42989.483218042995,13.062348029072668,15.153846153846153,0.18589499999999998,6058.230099320412,0.1839456558227539,2899.0220559668596,0.171251855223312,0.5915500043256848,0.608898387323711,0.07076230328752658,0.05588464476989181,37327.19260107514,9.17800659262771,11.5,0,2,2,22,1331.5,41.5,30.7,0.6708407871198568,0.13338822762296182
+CAT (tuned + ensemble),4861.749095384891,1.0365588940106905,3035.7630585976663,0.9271931106606893,0.728920075896456,0.6586722747505791,0.13137203388016178,0.08732478746102613,26561.38119334446,53.29220521181529,15.26923076923077,0.178855,3259.4502482414246,0.9432240724563599,1394.4481643221718,0.6180738220288715,0.7992439551459185,0.7086230805879283,0.06547094816637722,0.051124014208117546,20607.594190900367,47.175332427485074,14.5,0,1,1,24,1329.0,37.0,27.4,0.6681574239713775,0.10571294421823016
+MNCA (tuned),11104.727954295966,0.5286179322462815,5977.187221364461,0.4814995680859316,0.7448490525012423,0.6254269268193292,0.12195590153519999,0.09981792358357669,78433.1838326286,30.191390941699975,15.403846153846153,0.18580999999999998,11212.920736551285,0.4423438310623169,6020.892835479641,0.4537708228509102,0.8177816514691232,0.6675386576808539,0.08289705547642268,0.05461255954908243,67721.7850080898,25.373417518222958,14.0,2,0,1,23,1326.1,39.2,26.8,0.6650268336314848,0.16427089788095786
+XGB (tuned + ensemble),1868.2429302564035,2.459977333362286,1131.3403541498637,3.0908212073715786,0.7784275988369416,0.7199035203924502,0.1486241682315518,0.10152906173151074,11151.77336671448,133.9681639809692,16.0,0.184395,1441.2213016748428,1.317580223083496,785.8975953484313,1.2809013038285242,0.8316341855574652,0.7356064100282319,0.0929411488865951,0.054690456125919155,11226.485943771408,74.70529109142498,13.5,0,0,2,24,1314.2,28.3,28.0,0.6511627906976745,0.08975992178572184
+CAT (default),173.12264615755814,0.2530022859573364,152.19981148640542,0.1746769705615129,0.7721520925382083,0.7330908057220229,0.14409596218236434,0.09317729878661425,503.5985284620293,11.812145121206303,16.096153846153847,0.18153,12.185660362243652,0.2613861560821533,5.736216485552211,0.13945330874004275,0.8708469956136012,0.8336733935247667,0.08972393497982317,0.048694739189140704,101.89620933712393,7.710770185087604,18.25,1,0,0,25,1312.3,33.9,39.1,0.6489266547406082,0.10632669819168675
+CAT (tuned),4861.749095384891,0.10756405500265268,3035.7630585976663,0.11792367197674246,0.7533425274831995,0.7249538160567979,0.14044086628366612,0.0943615544793235,26561.38119334446,7.02054598025311,16.53846153846154,0.179485,3259.4502482414246,0.05912292003631592,1394.4481643221718,0.05275041900206211,0.8504867849422679,0.791074414481965,0.08259970760162288,0.05413571715045103,20607.594190900367,2.886943526422101,16.0,0,0,1,25,1304.1,30.2,35.5,0.6386404293381037,0.08070978014425713
+GBM (tuned),1386.6633568268555,0.6239433655372033,855.5354588276103,0.6651164927916418,0.737683616453633,0.7394663884062609,0.14768864875816456,0.09950734711225898,10078.423717398238,42.45419816933193,17.057692307692307,0.18125,1317.1929507255554,0.32417428493499756,691.4641967411458,0.2643910314838089,0.7826548575969626,0.7975693661177521,0.07901314149648692,0.06910038121186715,9059.644744264673,20.34411810847653,17.5,0,1,0,25,1291.8,34.5,33.7,0.6265652951699463,0.08667185975660585
+MNCA (tuned + ensemble),11104.727954295966,11.626816547833956,5977.187221364461,9.077822725159232,0.6933896363411076,0.6487475573068993,0.13992523616123162,0.11211871841613495,78433.1838326286,625.8156905319904,17.115384615384617,0.196755,11212.920736551285,9.569536089897156,6020.892835479641,8.47783782257851,0.722368050936999,0.6598703101341699,0.07839828824042583,0.06140957049904748,67721.7850080898,462.66839224080115,15.75,0,1,2,23,1292.4,35.8,33.6,0.6252236135957067,0.11906413304593146
+XGB (tuned),1868.2429302564035,0.6344939470291138,1131.3403541498637,0.845858557757576,0.8187393857188691,0.7534881250355465,0.15104001847209594,0.10532513671555101,11151.77336671448,35.07178353015141,17.192307692307693,0.18658,1441.2213016748428,0.2507840394973755,785.8975953484313,0.21279594317419753,0.9017943832788667,0.7297268207379917,0.10088305879986287,0.06269228828209442,11226.485943771408,15.348416723363933,15.5,1,0,0,25,1291.3,30.0,33.7,0.6234347048300537,0.10484293084036392
+TABM (default),26.77778566800631,0.20130640726823074,16.983704596070464,0.1991903141757489,0.7120650416184634,0.6923516563380268,0.15893030218385468,0.11297435774742692,212.38711719883958,11.49822311803021,17.53846153846154,0.18572,20.835053324699402,0.1561201810836792,11.598826273977398,0.14759862387674094,0.7417274447397321,0.7700731238711777,0.09071877675178053,0.06275561870896407,109.84095896574044,8.743598466674035,18.0,1,0,1,24,1282.8,35.4,33.8,0.6153846153846154,0.12718248968645465
+EBM (tuned + ensemble),2866.551095375648,0.3536289380146907,1716.6253639310917,0.31694700723366953,0.8144475987903695,0.7258303382715928,0.16832760766481616,0.12971565077834002,16005.580219570395,21.915829440477204,17.846153846153847,0.18934,1826.563945055008,0.30398499965667725,1142.3094021828929,0.2383204663966832,0.9368603191709515,0.8837415909105775,0.07492264885303435,0.04550198604731012,15335.47890204938,12.950523458443941,20.5,0,3,1,22,1278.3,28.6,31.2,0.6082289803220036,0.12613924248740332
+NN_TORCH (tuned + ensemble),8269.36125588417,3.6064914281551657,3548.397093003735,3.1069146452932506,0.8005534385996745,0.7705080916586386,0.1529691514160514,0.09729166486770355,54687.42205511106,206.78432079512532,19.153846153846153,0.186795,7021.069797754288,2.925987720489502,2792.139673756541,2.175346818589723,0.9508009219506811,0.9124525941492485,0.08120693152281688,0.06776685850668523,44532.50472025028,168.68500762383525,19.5,0,0,2,24,1249.2,36.0,28.4,0.5778175313059034,0.08657128307328764
+REALMLP (tuned),17853.666671056013,0.2349767226439256,6069.468633023759,0.2967994896112342,0.7992732991174807,0.7977993865865203,0.16124952982559976,0.1082766257578261,114488.59335090281,15.66992951032636,20.076923076923077,0.179705,10652.850141763687,0.22168636322021484,5575.693006637695,0.23576453293502733,0.895201963654964,0.9731977379559409,0.11192425937454437,0.08568734793187416,88499.22644289241,12.792606929662101,20.0,0,1,0,25,1231.8,37.4,28.6,0.556350626118068,0.07665354220614971
+EBM (tuned),2866.551095375648,0.04391795855302077,1716.6253639310917,0.042622423373582956,0.8796773591220505,0.7910593906123241,0.17682875196366907,0.13680041849059105,16005.580219570395,2.6200464262605005,20.21153846153846,0.19075999999999999,1826.563945055008,0.031216979026794434,1142.3094021828929,0.02993136666804911,1.0,1.0,0.08520888190451481,0.06958046400412875,15335.47890204938,1.3258183958395588,23.25,1,0,1,24,1229.9,36.9,37.8,0.5532200357781754,0.11052293589229499
+EBM (default),11.305762657752403,0.07217896901644193,6.708906519026521,0.08642030214738769,0.8468070324259693,0.8042920507016109,0.1835565890773308,0.13945315415243004,76.4056328740172,4.809914969274192,21.5,0.19205,8.478393912315369,0.060648202896118164,5.571264059656597,0.07129895504487138,1.0,1.0,0.10192934818149862,0.0536012172764834,66.99828845613939,3.601780880785588,24.25,1,0,0,25,1206.2,37.4,32.9,0.5232558139534884,0.10548819198142113
+FASTAI (tuned + ensemble),2663.3156125178702,9.71036501114185,1626.2333617037298,10.35375213848753,0.7698173487890432,0.7766239266765838,0.1868895729064691,0.11051193072704785,20608.72361153851,575.9084247062348,21.596153846153847,0.18179499999999998,2361.516105532646,9.277374625205994,1350.0449662692658,6.569572430045136,0.9823907390988809,0.9882257597451694,0.09835962628115286,0.07519988054335719,13001.178549428518,549.1440903510916,23.0,0,0,0,26,1203.9,41.1,36.0,0.5210196779964222,0.08398545799471152
+MNCA (default),29.761946293023918,0.45968284056736874,15.939524477151517,0.3666958147944243,0.8169283513779425,0.811254423026345,0.1897696514350087,0.10664147250973509,187.22763978272167,23.803143758716253,22.076923076923077,0.19357,23.457369804382324,0.36945486068725586,13.667502577653629,0.33894040253174296,0.9808195818002508,0.9565271309741105,0.13240284359564197,0.08931940966654422,154.11579909730304,19.118442709424343,20.5,1,1,0,24,1194.4,32.1,30.3,0.5098389982110912,0.10393125600187421
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diff --git a/data/lite/tabpfn-cls/tuning-impact-elo-horizontal.pdf b/data/lite/tabpfn-cls/tuning-impact-elo-horizontal.pdf
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diff --git a/data/lite/tabpfn-reg/figures/critical-diagram.pdf b/data/lite/tabpfn-reg/figures/critical-diagram.pdf
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diff --git a/data/lite/tabpfn-reg/leaderboard.tex b/data/lite/tabpfn-reg/leaderboard.tex
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index 0000000000000000000000000000000000000000..99c33cc0a82eec6afeff9f1fed04dd3991c24c8c
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+++ b/data/lite/tabpfn-reg/leaderboard.tex
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+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+AutoGluon 1.3 (4h) & \textcolor{gold}{\textbf{1748${}_{-74,+96}$}} & 0.691 & \textcolor{gold}{\textbf{6.3}} & 4.3 & 0 & 4.9\% & 4226.02 & 5.16 \\
+TabPFNv2 (T+E) & \textcolor{silver}{\textbf{1745${}_{-94,+79}$}} & \textcolor{silver}{\textbf{0.758}} & \textcolor{silver}{\textbf{6.6}} & \textcolor{gold}{\textbf{1.4}} & \textcolor{gold}{\textbf{5}} & \textcolor{gold}{\textbf{0.7\%}} & 6601.21 & 73.56 \\
+RealMLP (T+E) & \textcolor{bronze}{\textbf{1739${}_{-74,+81}$}} & \textcolor{silver}{\textbf{0.758}} & \textcolor{silver}{\textbf{6.6}} & 5.1 & 0 & \textcolor{bronze}{\textbf{3.9\%}} & 7718.29 & 4.52 \\
+TabDPT (D) & 1680${}_{-60,+121}$ & \textcolor{gold}{\textbf{0.787}} & 8.1 & \textcolor{bronze}{\textbf{4.0}} & \textcolor{silver}{\textbf{1}} & 4.5\% & 36.28 & 33.43 \\
+CatBoost (T+E) & 1641${}_{-68,+86}$ & 0.605 & 9.4 & 8.6 & 0 & 6.3\% & 3301.07 & 1.32 \\
+LightGBM (T+E) & 1626${}_{-67,+68}$ & 0.515 & 10.1 & 8.7 & 0 & 7.5\% & 997.12 & 3.79 \\
+CatBoost (T) & 1593${}_{-57,+101}$ & 0.572 & 10.9 & 6.1 & 0 & 6.4\% & 3301.07 & 0.13 \\
+TabPFNv2 (T) & 1589${}_{-79,+76}$ & 0.524 & 11.1 & \textcolor{silver}{\textbf{3.9}} & 0 & \textcolor{silver}{\textbf{3.6\%}} & 6601.21 & 0.59 \\
+TabM (T+E) & 1557${}_{-86,+62}$ & 0.487 & 12.6 & 9.5 & 0 & 4.8\% & 4228.53 & 1.45 \\
+ModernNCA (T+E) & 1526${}_{-67,+82}$ & 0.371 & 13.3 & 6.2 & 0 & 9.4\% & 9360.97 & 7.66 \\
+XGBoost (T+E) & 1498${}_{-65,+58}$ & 0.453 & 14.9 & 13.9 & 0 & 8.0\% & 911.22 & 3.53 \\
+XGBoost (T) & 1498${}_{-67,+60}$ & 0.407 & 14.4 & 13.4 & 0 & 8.0\% & 911.22 & 0.53 \\
+TabPFNv2 (D) & 1484${}_{-89,+88}$ & 0.424 & 15.3 & \textcolor{bronze}{\textbf{4.0}} & 0 & 5.7\% & 8.97 & 0.84 \\
+TabM (T) & 1458${}_{-70,+65}$ & 0.361 & 16.0 & 12.5 & 0 & 5.6\% & 4228.53 & 0.17 \\
+LightGBM (T) & 1455${}_{-60,+80}$ & 0.381 & 16.0 & 14.0 & 0 & 8.8\% & 997.12 & 0.64 \\
+CatBoost (D) & 1453${}_{-74,+61}$ & 0.376 & 16.1 & 10.9 & 0 & 9.7\% & 9.39 & 0.10 \\
+RealMLP (T) & 1449${}_{-64,+64}$ & 0.295 & 16.6 & 14.9 & 0 & 6.6\% & 7718.29 & 0.42 \\
+ModernNCA (D) & 1428${}_{-74,+97}$ & 0.061 & 17.3 & 13.7 & 0 & 11.7\% & 27.26 & 0.29 \\
+ExtraTrees (T+E) & 1406${}_{-64,+83}$ & 0.241 & 18.0 & 11.4 & 0 & 13.2\% & 613.95 & 0.95 \\
+ExtraTrees (T) & 1405${}_{-66,+68}$ & 0.210 & 18.3 & 10.4 & 0 & 13.5\% & 613.95 & 0.13 \\
+RealMLP (D) & 1353${}_{-77,+92}$ & 0.157 & 20.1 & 12.7 & 0 & 8.5\% & 41.85 & 0.30 \\
+TabM (D) & 1348${}_{-82,+63}$ & 0.319 & 20.6 & 17.3 & 0 & 7.7\% & 17.97 & 0.15 \\
+TorchMLP (T+E) & 1317${}_{-81,+86}$ & 0.136 & 21.9 & 20.3 & 0 & 9.6\% & 5937.36 & 1.41 \\
+ModernNCA (T) & 1262${}_{-70,+79}$ & 0.125 & 23.7 & 21.2 & 0 & 12.7\% & 9360.97 & 0.52 \\
+TorchMLP (T) & 1244${}_{-81,+71}$ & 0.139 & 24.6 & 21.0 & 0 & 10.0\% & 5937.36 & 0.12 \\
+RandomForest (T+E) & 1236${}_{-86,+88}$ & 0.122 & 25.0 & 21.4 & 0 & 15.2\% & 628.51 & 0.81 \\
+EBM (T+E) & 1223${}_{-82,+67}$ & 0.186 & 25.4 & 12.7 & 0 & 15.2\% & 2317.66 & 0.14 \\
+ExtraTrees (D) & 1185${}_{-87,+70}$ & 0.085 & 27.0 & 23.8 & 0 & 15.3\% & 0.43 & 0.12 \\
+LightGBM (D) & 1183${}_{-65,+69}$ & 0.016 & 27.0 & 26.5 & 0 & 12.3\% & 4.41 & 0.36 \\
+RandomForest (T) & 1170${}_{-71,+82}$ & 0.087 & 27.4 & 24.0 & 0 & 16.3\% & 628.51 & 0.15 \\
+FastaiMLP (T+E) & 1154${}_{-80,+67}$ & 0.000 & 27.9 & 27.5 & 0 & 14.2\% & 2163.39 & 7.62 \\
+EBM (T) & 1154${}_{-70,+86}$ & 0.151 & 27.7 & 5.9 & \textcolor{silver}{\textbf{1}} & 16.2\% & 2317.66 & 0.02 \\
+XGBoost (D) & 1128${}_{-81,+84}$ & 0.000 & 29.0 & 28.2 & 0 & 13.5\% & 3.97 & 0.24 \\
+FastaiMLP (T) & 1116${}_{-81,+62}$ & 0.000 & 29.4 & 29.0 & 0 & 14.7\% & 2163.39 & 0.71 \\
+EBM (D) & 1073${}_{-79,+80}$ & 0.074 & 30.6 & 28.5 & 0 & 16.9\% & 9.65 & 0.06 \\
+TorchMLP (D) & 1049${}_{-84,+70}$ & 0.010 & 31.3 & 29.1 & 0 & 14.2\% & 20.50 & 0.13 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.000 & 32.6 & 31.7 & 0 & 18.0\% & 0.67 & 0.12 \\
+FastaiMLP (D) & 978${}_{-70,+72}$ & 0.000 & 33.0 & 32.6 & 0 & 19.9\% & 6.88 & 0.45 \\
+Linear (T) & 457${}_{-153,+157}$ & 0.000 & 40.3 & 40.2 & 0 & 35.4\% & 154.23 & 0.08 \\
+Linear (T+E) & 455${}_{-135,+133}$ & 0.000 & 40.2 & 40.1 & 0 & 35.4\% & 154.23 & 0.16 \\
+KNN (T+E) & 381${}_{-144,+134}$ & 0.000 & 40.9 & 40.8 & 0 & 41.0\% & 2.26 & 0.15 \\
+Linear (D) & 320${}_{-143,+135}$ & 0.000 & 41.5 & 41.4 & 0 & 37.2\% & 3.09 & 0.09 \\
+KNN (T) & 282${}_{-167,+114}$ & 0.000 & 41.7 & 41.7 & 0 & 41.6\% & 2.26 & 0.03 \\
+KNN (D) & 21${}_{-227,+181}$ & 0.000 & 43.4 & 43.4 & 0 & 45.3\% & 0.05 & 0.03 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/lite/tabpfn-reg/tabarena_leaderboard.csv b/data/lite/tabpfn-reg/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..14983bb513a1372829296b8d291cbf6adc29db0a
--- /dev/null
+++ b/data/lite/tabpfn-reg/tabarena_leaderboard.csv
@@ -0,0 +1,45 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+AutoGluon 1.3 (4h),6507.599508047104,9.172560078757149,4878.0794801313095,6.788904878407515,0.3085910704411772,0.37549473479505696,0.0486082452707653,0.04602529164641145,107683.46631145268,541.3270644129536,6.285714285714286,4.10013,6398.424601554871,2.726226568222046,4226.0222111993835,5.158970598131418,0.23133371369265832,0.28724492162524173,0.02087460906626326,0.03128273627544052,97437.24755684618,393.2261425771175,5.0,0,1,1,5,1748.4,95.4,73.8,0.8770764119601329,0.23509585652442794
+REALMLP (tuned + ensemble),17294.968933514185,3.506439651761736,7783.542231376436,5.804296668854893,0.24215826947341315,0.392343081586603,0.038845120274675446,0.04229126594047559,213038.26238584044,318.83634333585195,6.571428571428571,4.1572,7049.241462230682,3.1589651107788086,7718.289864704748,4.516099027530876,0.1939333280527935,0.370923207918769,0.02624438674143237,0.040913538766118675,158412.06608302437,260.1484504479807,6.0,0,0,2,5,1739.3,81.0,73.2,0.8704318936877077,0.1967032967032967
+TABPFNV2 (tuned + ensemble),13265.934764044625,116.34418719155448,6406.189740569957,86.56063966774829,0.2415939115786189,0.22530714141967798,0.007012905956065296,0.011864420363929354,177767.73115525587,6824.09057332562,6.571428571428571,3.91474,7449.2451322078705,31.52299928665161,6601.20821444942,73.56061810307183,0.06610997682360657,0.0,0.0,0.0,161815.97140652352,4111.6099760549805,1.0,5,0,0,2,1745.2,78.2,93.7,0.8704318936877077,0.7289285714285715
+TABDPT (default),58.233478920800344,21.44670605659485,36.772525520044425,34.985783087632136,0.21328360616453232,0.41695813127194875,0.044633371588008654,0.04493187524163665,864.7561387477256,2038.6761504521721,8.142857142857142,4.24006,45.445554971694946,18.623436212539673,36.28271871618091,33.43014439125232,0.18149452492987356,0.49769575805604005,0.01506955690923395,0.0493188827159149,780.9167218453919,1672.754347061846,7.0,1,0,0,6,1680.4,120.6,59.3,0.8338870431893688,0.24873194891991884
+CAT (tuned + ensemble),10219.439358098167,1.2273011548178536,6237.092384383964,1.3764643405546468,0.3954429862751284,0.5225342283977304,0.06330676638407992,0.06060158351003724,151904.27519951403,99.66578971371487,9.428571428571429,4.34931,10386.55150604248,0.8227088451385498,3301.0682722746046,1.3169555135467037,0.36849553490903875,0.5320058320162349,0.02223613591863527,0.06198224787320109,136008.98155550173,66.23708934234935,10.0,0,0,0,7,1640.8,85.7,67.8,0.8039867109634552,0.11581275866990152
+GBM (tuned + ensemble),1519.5935083457402,15.220876693725586,1075.2207780922756,8.071953392174887,0.48543215706848314,0.557311976668291,0.07463717866296053,0.06555042697877127,24050.082073960144,732.0577620840215,10.142857142857142,4.23184,1280.6412847042084,2.1158533096313477,997.1187061258496,3.7868055514991283,0.3027896779514977,0.5076577280238057,0.021001080160577668,0.053508861901668286,21944.50900109299,305.1869734172427,8.0,0,0,0,7,1626.5,67.7,66.1,0.7873754152823921,0.1153986155865855
+CAT (tuned),10219.439358098167,0.20652290752955846,6237.092384383964,0.17520631671032216,0.42772839942623186,0.5533378290896562,0.06381219816795006,0.0719249798648832,151904.27519951403,14.407039225588733,10.857142857142858,4.33359,10386.55150604248,0.06416535377502441,3301.0682722746046,0.12532295659184456,0.5120020111687084,0.5261838554238772,0.03215961957864544,0.06130394854912813,136008.98155550173,8.436775680044018,10.0,0,1,0,6,1592.9,100.5,56.9,0.770764119601329,0.16386800334168755
+TABPFNV2 (tuned),13265.934764044625,6.892481701714652,6406.189740569957,3.565075900288693,0.4760165239054399,0.4686126452622397,0.03576915723256963,0.05812772216726451,177767.73115525587,371.838691600217,11.142857142857142,4.06636,7449.2451322078705,0.3009166717529297,6601.20821444942,0.5877278745174408,0.3837541852536315,0.2739588480569308,0.037284762407548744,0.02674626900933208,161815.97140652352,33.800294797494224,4.0,0,2,1,4,1588.8,75.8,78.1,0.7641196013289037,0.25375795479482116
+TABM (tuned + ensemble),10587.073945794788,1.1654579980032784,6469.982531368069,1.4219714334130276,0.5128718562532983,0.5637799557572225,0.04752740423249023,0.07247049692594254,159898.94570827132,89.13274144984146,12.571428571428571,4.37433,7112.7270860672,0.7802078723907471,4228.525932170142,1.4474226149502178,0.35916463746804045,0.5514632052105218,0.04867875413017375,0.06046439799308363,130257.5121002907,89.7509542969153,11.0,0,0,0,7,1557.2,61.6,85.9,0.7308970099667774,0.1049611763897478
+MNCA (tuned + ensemble),11347.046411412102,5.150112356458392,8870.126225994847,7.01101400475912,0.629033079050954,0.6047277718923637,0.09420521732745024,0.08630788018224085,198709.11442978613,432.41908111150343,13.285714285714286,5.17584,9893.872790813446,3.8368866443634033,9360.97195459853,7.658456375974857,0.5415267425434339,0.7216078202690936,0.028945351266741137,0.04011651282919255,219316.22015007408,507.3929797993456,13.0,0,1,0,6,1525.9,81.7,66.2,0.7142857142857143,0.16165797422647987
+XGB (tuned),1964.623684678759,0.8363437311989921,1258.1348891579723,1.0162165524968663,0.5931661199323147,0.7041657661531696,0.07964264245312702,0.07605759147806708,29068.33225029261,76.75307718831554,14.428571428571429,4.27126,1646.6080114841461,0.44248437881469727,911.2186578282138,0.526588345238556,0.6620816436761664,0.6725769533673283,0.028796944630651078,0.066243759828395,28408.724850613697,33.71919899313387,14.0,0,0,0,7,1498.0,59.4,66.2,0.6877076411960132,0.0748299319727891
+XGB (tuned + ensemble),1964.623684678759,5.225094931466239,1258.1348891579723,6.231431906809619,0.5469310959609862,0.7006561259920765,0.080152008776187,0.07682133754513054,29068.33225029261,469.8791112336729,14.857142857142858,4.28931,1646.6080114841461,2.946662425994873,911.2186578282138,3.5316545373471357,0.5452119463463313,0.7076179193239929,0.026750868930337512,0.07131817083383857,28408.724850613697,165.90428541165622,15.0,0,0,0,7,1498.3,57.2,64.7,0.6777408637873754,0.07177332471450118
+TABPFNV2 (default),10.536325999668666,0.6483856269291469,8.132158828132207,0.7522934493235269,0.5759929931993246,0.5072415769751218,0.057203539607642365,0.09660218627950926,179.00806062175934,48.22880991521019,15.285714285714286,3.96968,9.203422546386719,0.44887351989746094,8.969435810057584,0.8365089530186938,0.4230930305474229,0.2813323772445926,0.05552265207828644,0.034900636525002166,193.52031139495512,52.107250060845345,3.0,0,2,2,3,1483.7,87.2,88.6,0.6677740863787376,0.252437641723356
+TABM (tuned),10587.073945794788,0.15967791421072824,6469.982531368069,0.17435700805504006,0.6392492711824916,0.6999695665076091,0.05563034885495726,0.08940263902914376,159898.94570827132,12.098100035047194,16.0,4.38922,7112.7270860672,0.1314229965209961,4228.525932170142,0.17298539846653752,0.4652267330424026,0.744542337736779,0.05191534338451931,0.08006597714469382,130257.5121002907,9.015021272319185,15.0,0,0,0,7,1457.7,64.7,69.6,0.6511627906976745,0.08012166405023548
+GBM (tuned),1519.5935083457402,1.4627540792737688,1075.2207780922756,1.050105430857932,0.6186778290078211,0.7683445449024502,0.08826070516525375,0.09138656595801721,24050.082073960144,90.68047060933284,16.0,4.36733,1280.6412847042084,0.19193363189697266,997.1187061258496,0.6355418274734194,0.5552965190223861,0.7809690118769635,0.03499422009754971,0.07637313803793273,21944.50900109299,25.03430046335168,15.0,0,0,0,7,1455.1,79.1,60.0,0.6511627906976745,0.07125493554064984
+CAT (default),30.187910761151993,0.21431371143886022,21.23474421835824,0.22233607116297832,0.6237900244125646,0.7828506352265132,0.09689377152933412,0.09771959149576696,487.8800741332821,18.478250211890124,16.142857142857142,4.47999,43.05822467803955,0.04317522048950195,9.390118294059649,0.10484877571411665,0.5847419575275037,0.8647501675552545,0.045915518799006794,0.11727889352322712,318.4902885443583,6.227518140238661,18.0,0,0,0,7,1452.6,61.0,73.2,0.6478405315614618,0.09190658109162811
+REALMLP (tuned),17294.968933514185,0.1830120086669922,7783.542231376436,0.3259796662543578,0.7047967586156892,0.7455274593429628,0.06611928626088935,0.08706588082885992,213038.26238584044,18.10152693439293,16.571428571428573,4.49659,7049.241462230682,0.18346905708312988,7718.289864704748,0.41665478856739646,0.7895709633169843,0.888035701857616,0.03133979505568851,0.06965496351407507,158412.06608302437,16.898709432908465,17.0,0,0,0,7,1449.0,63.9,63.9,0.6378737541528239,0.06694456730060445
+MNCA (default),32.55938308579581,0.19486018589564733,24.957505841642334,0.25196370179633515,0.9394939857609429,0.7698114045810972,0.1167268604145739,0.1003795869866352,579.4685759941773,15.594763357138524,17.285714285714285,5.11399,27.96918511390686,0.14082908630371094,27.260414341039827,0.28976407064988385,1.0,0.8968599174854596,0.03164135806644719,0.10122870662762316,549.2641916961603,14.335706179717322,18.0,0,0,0,7,1427.5,96.4,73.5,0.6212624584717608,0.07283386960405594
+XT (tuned + ensemble),653.1345855508532,1.3570618970053536,577.6942803326062,1.4658800281842608,0.7590185126625372,0.7362657403903793,0.13182107507552596,0.10321816124521692,11988.325985095387,91.31150878008751,18.0,5.19051,619.4238421916962,1.1150686740875244,613.9510258467136,0.9522022685938181,1.0,0.8933306559798235,0.019723175236079915,0.0969336948409988,13486.685259241041,88.45578138045335,18.0,0,0,0,7,1405.8,82.8,63.4,0.6046511627906976,0.08802681992337165
+XT (tuned),653.1345855508532,0.19052549770900182,577.6942803326062,0.20596209279824323,0.7900820708578837,0.7050663135559584,0.13531068870954055,0.10206565880892562,11988.325985095387,12.425140461101606,18.285714285714285,5.21222,619.4238421916962,0.12293267250061035,613.9510258467136,0.13470561454520408,1.0,0.8750587070440755,0.019404407055781392,0.07760478346112754,13486.685259241041,12.271457921927672,16.0,0,0,0,7,1405.3,67.7,65.4,0.5980066445182725,0.0957717275170248
+REALMLP (default),85.97223772321429,0.1575484275817871,39.12093352642527,0.2743856804074748,0.8426262409401332,0.811431445405329,0.08492984112267965,0.09970894106924409,1070.0293170023945,15.38773372916076,20.142857142857142,4.72117,38.86532187461853,0.15464305877685547,41.84851132799618,0.30457759331800266,0.9775778014564183,1.0,0.05630051064737385,0.09045562976994424,871.512756220395,17.475355288117672,23.0,0,0,0,7,1352.9,91.2,76.2,0.5548172757475083,0.07870035761340109
+TABM (default),44.222292866025654,0.09508374759129115,33.52655463742946,0.14459166718033462,0.6807517607831785,0.8006243600538642,0.07746676233499121,0.11907088719148319,799.8890873088621,8.677317571690375,20.571428571428573,4.49975,33.95261740684509,0.09252333641052246,17.97321997410923,0.1450319163846654,0.6869868540300672,0.8949833068709946,0.05541802374036453,0.09871836929846278,614.4361778763692,5.7128774450352955,21.0,0,0,0,7,1348.3,62.2,81.5,0.5448504983388704,0.05791081251910745
+NN_TORCH (tuned + ensemble),12457.980165379387,1.2596015930175781,7008.335221238185,1.51976815453829,0.8644212313341298,0.8821930899105708,0.09635544815863366,0.13054872917279642,168590.23153378774,91.87300057791074,21.857142857142858,4.45415,7507.96986413002,0.7180907726287842,5937.361191915086,1.4061158013450725,1.0,0.9593685963016786,0.08724572872591152,0.09102294293315928,150677.82359954235,86.08980937276488,21.0,0,0,0,7,1317.1,85.1,80.9,0.5149501661129569,0.049260037598247694
+MNCA (tuned),11347.046411412102,0.3390509401048933,8870.126225994847,0.4537320246881022,0.8745416984197177,0.8809824771089103,0.12651248212292465,0.15226349694705085,198709.11442978613,28.22969633543519,23.714285714285715,5.75005,9893.872790813446,0.2758302688598633,9360.97195459853,0.5220087902191429,1.0,1.0,0.04948509220058683,0.10805583782920748,219316.22015007408,25.82571615254995,23.0,0,0,0,7,1262.4,78.9,69.6,0.4717607973421927,0.04720915415791192
+NN_TORCH (tuned),12457.980165379387,0.08400774002075195,7008.335221238185,0.11080666811510796,0.8608314951688506,0.8774455871830203,0.09957021763907885,0.15815621944343286,168590.23153378774,6.839931733747739,24.571428571428573,4.35043,7507.96986413002,0.06095480918884277,5937.361191915086,0.11905236169695854,1.0,1.0,0.09669761975708657,0.07439215953480095,150677.82359954235,6.913734822466806,26.0,0,0,0,7,1243.8,70.9,80.6,0.45182724252491696,0.04772219453276936
+RF (tuned + ensemble),801.2955491883414,1.11563321522304,613.3607596500245,1.26624268506503,0.877625154030774,0.8717206280423674,0.15150038451410816,0.13535733856884374,13220.038476594822,74.24085143974197,25.0,5.35575,630.387446641922,0.864675760269165,628.5083029798404,0.811527051576754,1.0,1.0,0.04140035298054934,0.16236919097501226,13750.799241897716,73.76895458081503,29.0,0,0,0,7,1235.9,87.3,85.2,0.4418604651162791,0.04683025041397519
+EBM (tuned + ensemble),5561.30316393716,0.11157175472804479,3175.063314528853,0.13502700552021676,0.8141909618196094,0.8896216728438898,0.15236144498178752,0.1898206774797642,73928.60900997189,8.51769019764669,25.428571428571427,4.4992,4016.161784172058,0.0706477165222168,2317.663761142634,0.13798382133245468,1.0,1.0,0.11314433759685272,0.19494069658306334,53673.002392900955,8.013142594445496,30.0,0,0,1,6,1223.0,66.2,82.0,0.4318936877076412,0.07850231001491505
+GBM (default),7.295478275844029,0.6803168909890311,5.774057228808984,0.7873917849760669,0.9842293100404953,0.998266642271873,0.12276167672393257,0.1375507325867256,123.55828625698926,63.969416972987915,27.0,4.60997,5.699137926101685,0.33568358421325684,4.411330137552167,0.3631429914514132,1.0,1.0,0.05861977343818392,0.12652549891600978,89.74197938321082,41.091864073072564,26.0,0,0,0,7,1183.2,68.3,64.9,0.3953488372093023,0.037689489160077395
+XT (default),0.6613446303776332,0.07277822494506836,0.42786099680297607,0.10136865529358885,0.9145279857275386,0.9203781708634962,0.15302309586297927,0.14325676648751842,9.71399832220294,6.093015438403495,27.0,5.21222,0.43534398078918457,0.059967994689941406,0.42798109873088297,0.1173228956758976,1.0,1.0,0.07346763789813626,0.16206852516277276,9.783143310259103,5.904437603010231,30.5,0,0,0,7,1185.2,69.7,86.7,0.3953488372093023,0.04208640225911888
+RF (tuned),801.2955491883414,0.1265991755894252,613.3607596500245,0.187274000354846,0.9129471887632264,0.8942717915846629,0.16343200728271487,0.15127595329353374,13220.038476594822,10.741823760393732,27.357142857142858,5.50373,630.387446641922,0.11566352844238281,628.5083029798404,0.1492024419788353,1.0,1.0,0.05545969936477957,0.19024547228638602,13750.799241897716,12.51650764501577,33.5,0,0,0,7,1170.5,82.0,70.5,0.38704318936877075,0.041677761427603766
+EBM (tuned),5561.30316393716,0.015260968889508928,3175.063314528853,0.017210140514576817,0.8494479068291391,0.8504733869471625,0.16192837219447462,0.23515542970497189,73928.60900997189,1.0432270754270017,27.714285714285715,4.53788,4016.161784172058,0.00881648063659668,2317.663761142634,0.01721968874335289,1.0,1.0,0.11467541102152934,0.2156961514785443,53673.002392900955,1.0,35.0,1,0,0,6,1154.3,85.2,69.3,0.3787375415282392,0.17073522404349473
+FASTAI (tuned + ensemble),2255.801782812391,4.754738875797817,1874.5041683637385,7.02561873695556,1.0,0.9970644338604485,0.14237984020932382,0.17188628389751054,39821.805599866035,393.3305755649491,27.857142857142858,5.22937,2167.720710515976,4.366992950439453,2163.3939226706348,7.6216664328171975,1.0,1.0,0.09544576825940065,0.16368526688049675,44341.68565486565,365.3580675403427,28.0,0,0,0,7,1154.5,66.9,79.2,0.3754152823920266,0.03638504352790067
+XGB (default),5.3146242414202005,0.2511120523725237,3.9458596806008757,0.36393582726260965,1.0,0.9964160464678224,0.13461597663145097,0.17323981254075252,88.641308375137,25.06965036903791,29.0,5.03782,4.206391334533691,0.25308847427368164,3.970691708705885,0.24026284837936607,1.0,1.0,0.06048068144140273,0.13909193858299207,83.06864309112483,14.470859449961994,27.0,0,0,0,7,1127.9,83.3,80.1,0.3488372093023256,0.03551231551231551
+FASTAI (tuned),2255.801782812391,0.49124997002737864,1874.5041683637385,0.6913652874014505,1.0,1.0,0.14682535076785339,0.20225951117254543,39821.805599866035,40.61138754197303,29.428571428571427,5.20475,2167.720710515976,0.4167633056640625,2163.3939226706348,0.7125644494366172,1.0,1.0,0.11250145730924221,0.2028343782558529,44341.68565486565,41.0657519171911,30.0,0,0,0,7,1115.5,61.2,80.3,0.3388704318936877,0.03446883804026661
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+NN_TORCH (default),43.06239519800459,0.08627057075500488,31.428014747890156,0.11590329450401095,0.9903234435493359,0.9826874022847564,0.14240631859026293,0.22471185375084687,708.3643874980628,7.238187531162345,31.285714285714285,4.48918,41.744338512420654,0.06643533706665039,20.501721600246,0.12975651770830154,1.0,1.0,0.1279603352562162,0.12823638255534808,464.3796175172265,6.690027290059689,34.0,0,0,0,7,1049.0,69.1,83.5,0.2956810631229236,0.034315758971465174
+RF (default),1.6488038471766882,0.07487589972359794,0.7340231092118259,0.1055457863320713,1.0,0.9934710488617512,0.17986721711507,0.20097685324182807,19.421218249481452,6.45143964546498,32.642857142857146,5.50373,0.5004377365112305,0.060329437255859375,0.6685572767188281,0.11783093214035034,1.0,1.0,0.10385640724520351,0.20419806328614365,11.221774322893834,5.842398233666076,35.0,0,0,0,7,1000.0,0.0,0.0,0.26411960132890366,0.03157332807971695
+FASTAI (default),8.895496198109218,0.4506887027195522,6.745758004706619,0.5305722397771327,1.0,1.0,0.1992892524760812,0.2546651622823661,149.59972016881048,35.25519751231355,33.0,6.27121,7.010618448257446,0.3927772045135498,6.882870030736257,0.45197373548430714,1.0,1.0,0.11103713173333196,0.30980814568049436,155.75215591877054,35.82998039822552,32.0,0,0,0,7,977.9,71.9,69.6,0.2558139534883721,0.030677198284032307
+LR (tuned + ensemble),151.49413922854833,0.12385766846793038,140.2291087325694,0.2034921921233621,1.0,1.0,0.3536775263323216,0.5794878220593526,2871.3444089692503,12.750390729258724,40.214285714285715,7.90301,151.6921091079712,0.07966971397399902,154.22778905270818,0.15536207236991023,1.0,1.0,0.27398044406077504,0.6617005914731261,3356.9082636752537,7.635360610075989,39.0,0,0,0,7,455.3,132.5,134.8,0.08803986710963455,0.024907046897470975
+LR (tuned),151.49413922854833,0.0357013429914202,140.2291087325694,0.05877661513349659,1.0,1.0,0.3541042830323525,0.5805259668922418,2871.3444089692503,3.4075401142248447,40.285714285714285,7.95008,151.6921091079712,0.03558516502380371,154.22778905270818,0.07586279315148999,1.0,1.0,0.274940797173291,0.6653082983995465,3356.9082636752537,3.222681902578225,40.5,0,0,0,7,456.9,156.3,152.8,0.08637873754152824,0.024885473611305407
+KNN (tuned + ensemble),4.365503992353167,0.1759951114654541,2.503089425947226,0.17631688214986382,1.0,1.0,0.4095786597069403,0.743043049481299,60.321121177196595,11.281300197479359,40.857142857142854,8.84074,2.312436103820801,0.1065065860748291,2.2580906778514502,0.14868705810425048,1.0,1.0,0.42992491292377444,0.8312484819939865,51.96556010372688,12.473272386123199,42.0,0,0,0,7,381.1,133.7,143.1,0.07308970099667775,0.0245346688503645
+LR (default),3.9944423266819546,0.07546016148158483,3.291819429311604,0.12482651745436109,1.0,1.0,0.371612807074338,0.658400915114039,71.50131231042234,8.781090197151686,41.5,8.07907,2.9657371044158936,0.0455625057220459,3.0870405547822,0.0889892689883709,1.0,1.0,0.27343206074359794,0.6653082514030221,67.40864758081219,3.589990624937666,41.0,0,0,0,7,319.8,134.2,142.4,0.05813953488372093,0.024136808689654215
+KNN (tuned),4.365503992353167,0.03152152470179966,2.503089425947226,0.031078100848287667,1.0,1.0,0.41647808374429435,0.7824790590876856,60.321121177196595,2.033314994156082,41.714285714285715,9.11406,2.312436103820801,0.014968395233154297,2.2580906778514502,0.02987703639352155,1.0,1.0,0.4343387863461754,0.8773703541691247,51.96556010372688,1.9337322466384677,42.0,0,0,0,7,282.1,113.2,166.7,0.053156146179401995,0.02400393073150548
+KNN (default),0.06725828988211495,0.02155137062072754,0.043712475995924135,0.02795156394158391,1.0,1.0,0.45340288690514835,0.9405484140280839,1.0,1.6852534934851076,43.42857142857143,9.84077,0.04459524154663086,0.014516353607177734,0.045943402958487325,0.029731415464968502,1.0,1.0,0.4698333732046267,1.0,1.0,1.6403093647746019,44.0,0,0,0,7,20.6,180.5,226.4,0.013289036544850499,0.02304034592747015
diff --git a/data/lite/tabpfn-reg/tuning-impact-elo-horizontal.pdf b/data/lite/tabpfn-reg/tuning-impact-elo-horizontal.pdf
new file mode 100644
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diff --git a/data/lite/tabpfn-reg/tuning-impact-elo-horizontal.png.zip b/data/lite/tabpfn-reg/tuning-impact-elo-horizontal.png.zip
new file mode 100644
index 0000000000000000000000000000000000000000..4c40ae41478b8e43d4fe239a012e7b8d51ea0421
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+++ b/data/lite/tabpfn-reg/tuning-impact-elo-horizontal.png.zip
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+version https://git-lfs.github.com/spec/v1
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+size 135623
diff --git a/data/lite/tabpfn-tabicl-cls/figures/critical-diagram.pdf b/data/lite/tabpfn-tabicl-cls/figures/critical-diagram.pdf
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diff --git a/data/lite/tabpfn-tabicl-cls/figures/critical-diagram.png.zip b/data/lite/tabpfn-tabicl-cls/figures/critical-diagram.png.zip
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index 0000000000000000000000000000000000000000..ee6c1a909c106e11df9c28c59d1ea06ad8d90858
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+version https://git-lfs.github.com/spec/v1
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diff --git a/data/lite/tabpfn-tabicl-cls/leaderboard.tex b/data/lite/tabpfn-tabicl-cls/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..470e5847143808188c07aa37c1d5e5b76d097272
--- /dev/null
+++ b/data/lite/tabpfn-tabicl-cls/leaderboard.tex
@@ -0,0 +1,53 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+TabPFNv2 (T+E) & \textcolor{gold}{\textbf{1507${}_{-45,+32}$}} & \textcolor{gold}{\textbf{0.700}} & \textcolor{gold}{\textbf{8.5}} & \textcolor{gold}{\textbf{2.8}} & \textcolor{gold}{\textbf{6}} & \textcolor{gold}{\textbf{8.0\%}} & 3361.32 & 51.67 \\
+AutoGluon 1.3 (4h) & \textcolor{silver}{\textbf{1434${}_{-31,+33}$}} & 0.440 & \textcolor{silver}{\textbf{11.0}} & 6.4 & 1 & \textcolor{silver}{\textbf{10.4\%}} & 2085.73 & 2.16 \\
+RealMLP (T+E) & \textcolor{bronze}{\textbf{1408${}_{-30,+28}$}} & 0.402 & \textcolor{bronze}{\textbf{12.1}} & 6.9 & 0 & 12.3\% & 5575.69 & 4.58 \\
+TabPFNv2 (T) & 1402${}_{-33,+32}$ & \textcolor{silver}{\textbf{0.544}} & 12.2 & \textcolor{bronze}{\textbf{4.5}} & 1 & 10.6\% & 3361.32 & 0.53 \\
+TabM (T+E) & 1392${}_{-40,+33}$ & 0.483 & 12.8 & 6.2 & 1 & 12.0\% & 2899.02 & 1.46 \\
+TabICL (D) & 1378${}_{-31,+37}$ & \textcolor{bronze}{\textbf{0.543}} & 13.4 & \textcolor{silver}{\textbf{3.9}} & \textcolor{silver}{\textbf{4}} & \textcolor{bronze}{\textbf{10.5\%}} & 9.07 & 2.01 \\
+LightGBM (T+E) & 1349${}_{-33,+29}$ & 0.319 & 14.8 & 11.6 & 0 & 14.1\% & 691.46 & 1.46 \\
+TabPFNv2 (D) & 1334${}_{-35,+35}$ & 0.499 & 15.5 & \textcolor{bronze}{\textbf{4.5}} & \textcolor{bronze}{\textbf{3}} & 12.1\% & 4.28 & 0.51 \\
+CatBoost (T+E) & 1332${}_{-36,+33}$ & 0.271 & 15.8 & 9.8 & 0 & 13.5\% & 1394.45 & 0.62 \\
+TabM (T) & 1330${}_{-31,+33}$ & 0.387 & 15.7 & 7.7 & 0 & 13.1\% & 2899.02 & 0.17 \\
+ModernNCA (T) & 1326${}_{-31,+31}$ & 0.255 & 16.0 & 6.4 & 2 & 12.7\% & 6020.89 & 0.45 \\
+XGBoost (T+E) & 1314${}_{-33,+29}$ & 0.222 & 16.6 & 11.4 & 0 & 15.2\% & 785.90 & 1.28 \\
+CatBoost (D) & 1308${}_{-32,+30}$ & 0.228 & 16.8 & 9.6 & 1 & 14.8\% & 5.74 & 0.14 \\
+CatBoost (T) & 1299${}_{-28,+42}$ & 0.247 & 17.2 & 13.3 & 0 & 14.4\% & 1394.45 & 0.05 \\
+ModernNCA (T+E) & 1292${}_{-35,+27}$ & 0.307 & 17.7 & 9.4 & 0 & 14.4\% & 6020.89 & 8.48 \\
+XGBoost (T) & 1290${}_{-26,+30}$ & 0.181 & 17.8 & 9.8 & 1 & 15.4\% & 785.90 & 0.21 \\
+LightGBM (T) & 1288${}_{-29,+30}$ & 0.262 & 17.6 & 11.9 & 0 & 15.1\% & 691.46 & 0.26 \\
+TabM (D) & 1281${}_{-25,+30}$ & 0.288 & 18.2 & 8.1 & 1 & 16.2\% & 11.60 & 0.15 \\
+EBM (T+E) & 1277${}_{-29,+37}$ & 0.186 & 18.4 & 8.2 & 0 & 17.2\% & 1142.31 & 0.24 \\
+TorchMLP (T+E) & 1247${}_{-28,+31}$ & 0.199 & 19.9 & 12.7 & 0 & 15.7\% & 2792.14 & 2.18 \\
+RealMLP (T) & 1231${}_{-29,+34}$ & 0.201 & 20.8 & 13.3 & 0 & 16.5\% & 5575.69 & 0.24 \\
+EBM (T) & 1227${}_{-32,+37}$ & 0.120 & 20.9 & 11.1 & 0 & 18.0\% & 1142.31 & 0.03 \\
+EBM (D) & 1207${}_{-35,+29}$ & 0.153 & 22.2 & 9.7 & 1 & 18.7\% & 5.57 & 0.07 \\
+FastaiMLP (T+E) & 1202${}_{-34,+29}$ & 0.230 & 22.3 & 12.4 & 0 & 19.1\% & 1350.04 & 6.57 \\
+ModernNCA (D) & 1188${}_{-28,+29}$ & 0.183 & 22.8 & 9.8 & 1 & 19.4\% & 13.67 & 0.34 \\
+TabDPT (D) & 1179${}_{-36,+29}$ & 0.258 & 23.5 & 7.1 & 1 & 18.4\% & 28.08 & 8.76 \\
+RealMLP (D) & 1179${}_{-31,+24}$ & 0.083 & 23.6 & 11.0 & 1 & 17.5\% & 35.27 & 0.25 \\
+TorchMLP (T) & 1168${}_{-36,+38}$ & 0.086 & 24.0 & 17.9 & 0 & 17.8\% & 2792.14 & 0.13 \\
+ExtraTrees (T+E) & 1155${}_{-36,+29}$ & 0.102 & 24.9 & 16.7 & 0 & 20.4\% & 378.34 & 1.37 \\
+FastaiMLP (T) & 1140${}_{-32,+30}$ & 0.113 & 25.5 & 14.7 & 0 & 20.7\% & 1350.04 & 0.61 \\
+XGBoost (D) & 1130${}_{-32,+28}$ & 0.032 & 26.0 & 21.9 & 0 & 19.1\% & 2.66 & 0.17 \\
+ExtraTrees (T) & 1128${}_{-26,+31}$ & 0.072 & 26.0 & 13.8 & 0 & 20.6\% & 378.34 & 0.15 \\
+RandomForest (T+E) & 1114${}_{-34,+28}$ & 0.057 & 27.0 & 14.8 & 0 & 21.0\% & 527.27 & 1.16 \\
+LightGBM (D) & 1098${}_{-30,+32}$ & 0.030 & 27.6 & 24.0 & 0 & 20.1\% & 3.01 & 0.18 \\
+RandomForest (T) & 1064${}_{-35,+30}$ & 0.023 & 29.2 & 22.2 & 0 & 21.9\% & 527.27 & 0.13 \\
+TorchMLP (D) & 1006${}_{-30,+39}$ & 0.030 & 31.7 & 26.7 & 0 & 24.8\% & 8.93 & 0.17 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.005 & 32.2 & 24.6 & 0 & 27.3\% & 0.44 & 0.06 \\
+FastaiMLP (D) & 1000${}_{-32,+30}$ & 0.019 & 32.2 & 27.7 & 0 & 24.6\% & 4.68 & 0.55 \\
+ExtraTrees (D) & 949${}_{-32,+36}$ & 0.015 & 34.1 & 29.1 & 0 & 29.3\% & 0.36 & 0.07 \\
+Linear (T+E) & 946${}_{-35,+33}$ & 0.055 & 34.2 & 15.0 & 1 & 31.3\% & 89.12 & 0.20 \\
+Linear (T) & 925${}_{-44,+35}$ & 0.035 & 34.9 & 24.7 & 0 & 32.0\% & 89.12 & 0.08 \\
+Linear (D) & 892${}_{-29,+36}$ & 0.025 & 36.0 & 33.0 & 0 & 33.3\% & 2.43 & 0.10 \\
+KNN (T+E) & 746${}_{-31,+45}$ & 0.000 & 39.9 & 33.1 & 0 & 47.7\% & 3.50 & 0.16 \\
+KNN (T) & 676${}_{-48,+42}$ & 0.000 & 41.2 & 28.3 & 0 & 50.2\% & 3.50 & 0.03 \\
+KNN (D) & 384${}_{-116,+61}$ & 0.000 & 44.3 & 44.2 & 0 & 59.5\% & 0.07 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/lite/tabpfn-tabicl-cls/tabarena_leaderboard.csv b/data/lite/tabpfn-tabicl-cls/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..df059e4121634d1f0f2b668e430505bc14507b44
--- /dev/null
+++ b/data/lite/tabpfn-tabicl-cls/tabarena_leaderboard.csv
@@ -0,0 +1,46 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+TABPFNV2 (tuned + ensemble),14624.10810300937,166.6936253951146,3837.093570770289,71.22922446033486,0.2999799624009034,0.37900004959687766,0.08031051995518385,0.05455935044977521,73712.17169915051,6326.444952096131,8.461538461538462,0.1749,8447.90291428566,59.9476752281189,3361.317280362242,51.668880171806535,0.21441671773452015,0.3480554663987826,0.03548854933740464,0.027745461982937078,32774.70384527536,4094.471326370238,5.5,6,1,3,16,1506.7,31.8,44.7,0.8304195804195804,0.358094093982103
+AutoGluon 1.3 (4h),5811.540137483524,3.0737958137805643,3715.2599271484114,3.044362316385498,0.5599854226187498,0.5617828950350577,0.10445421398736063,0.0599680756233326,37983.87924037632,165.48830983683717,11.01923076923077,0.17751,5213.819254875183,2.0895845890045166,2085.7270446537464,2.161349128608518,0.5600808158121042,0.5574000428410242,0.06078825021132733,0.06659288302785793,30345.10100676535,118.3021811054546,9.0,1,0,1,24,1433.5,32.8,30.7,0.7722902097902098,0.15518544636191695
+REALMLP (tuned + ensemble),17853.666671056013,5.060677555891184,6069.468633023759,5.352972929535142,0.5983187605703805,0.5697719962134024,0.1225244655490201,0.0697158337547381,114488.59335090281,312.9299756303972,12.115384615384615,0.180765,10652.850141763687,4.7910391092300415,5575.693006637695,4.577857176513236,0.5696681015084324,0.5408431020221902,0.06341551053522204,0.04576778429435464,88499.22644289241,286.2007603461078,11.5,0,1,2,23,1407.7,28.0,30.0,0.7473776223776224,0.1450476689017345
+TABPFNV2 (tuned),14624.10810300937,5.890562671881455,3837.093570770289,2.9216612176257093,0.4562783530638654,0.539616756848536,0.10636474393434797,0.07576264928807884,73712.17169915051,208.74723417223507,12.25,0.19132,8447.90291428566,0.5261704921722412,3361.317280362242,0.52729181819818,0.34109768962419973,0.6143846899224816,0.08017059655741715,0.050216477642017796,32774.70384527536,36.401677545743524,11.0,1,5,2,18,1402.3,31.6,32.9,0.7443181818181818,0.22277582323851844
+TABM (tuned + ensemble),7183.65333878994,2.169150572556716,3570.712955098323,1.861754182648195,0.5166525238887076,0.5534887980632361,0.11956349986158774,0.08309123144106954,42989.483218042995,121.47528176372664,12.846153846153847,0.18310500000000002,6058.230099320412,1.6269241571426392,2899.0220559668596,1.460880880345262,0.47136645123500964,0.5213443194889227,0.07336602828104005,0.040297742432766875,37327.19260107514,109.27107326295308,9.5,1,0,1,24,1392.2,32.7,39.4,0.7307692307692307,0.16156485134544094
+TABICL (default),21.54241045621725,2.6437206268310547,10.219926189931016,1.9528462364644343,0.45732829959408833,0.5557361534084676,0.10499055070178752,0.06310686388674132,140.84761892371782,137.0823191069963,13.365384615384615,0.177865,18.648964643478394,2.1483960151672363,9.073519792448254,2.007502492806867,0.46959481282562443,0.6652418475567421,0.05302697309380272,0.028777901101857722,117.57303754113096,107.76328227971135,13.5,4,2,1,19,1378.5,36.4,30.5,0.7189685314685315,0.2556026489858902
+GBM (tuned + ensemble),1386.6633568268555,2.647538212629465,855.5354588276103,2.6861264004396594,0.6806482110513491,0.7106186510085695,0.141362130997881,0.08782930779014528,10078.423717398238,182.57724180654643,14.788461538461538,0.18084,1317.1929507255554,2.008246660232544,691.4641967411458,1.4553717527055694,0.7005535661954161,0.7652961068739543,0.07965351598262033,0.06332006129013433,9059.644744264673,90.72358893879789,14.75,0,0,1,25,1349.4,28.6,33.0,0.6866258741258742,0.08652272628643233
+TABPFNV2 (default),12.006092300781837,1.2122490681134737,5.3148066442099084,0.6434373636958165,0.5013642769155608,0.6076681115357273,0.12052806276021125,0.08926430649670257,74.57934257049592,50.66248833495304,15.538461538461538,0.194575,9.273962259292603,0.4255213737487793,4.283948847917442,0.5110342254172261,0.3771347187714357,0.8078260737500734,0.07359963710195983,0.07618105155863322,62.366726819804335,28.80821414198816,14.5,3,2,2,19,1333.5,34.9,34.7,0.6695804195804196,0.22455622157365326
+TABM (tuned),7183.65333878994,0.2019361532651461,3570.712955098323,0.2168923191387062,0.6127855914191946,0.6153990479247885,0.130856425990661,0.09537399493661194,42989.483218042995,13.062348029072668,15.692307692307692,0.18589499999999998,6058.230099320412,0.1839456558227539,2899.0220559668596,0.171251855223312,0.5915500043256848,0.6288985041617685,0.09148128983327741,0.05588464476989181,37327.19260107514,9.17800659262771,12.0,0,2,2,22,1329.7,32.2,30.3,0.666083916083916,0.13000506341067208
+CAT (tuned + ensemble),4861.749095384891,1.0365588940106905,3035.7630585976663,0.9271931106606893,0.728920075896456,0.6786642048762535,0.13487992339677388,0.0909217961956513,26561.38119334446,53.29220521181529,15.76923076923077,0.178855,3259.4502482414246,0.9432240724563599,1394.4481643221718,0.6180738220288715,0.7992439551459185,0.7007978686240202,0.07405507598059591,0.051124014208117546,20607.594190900367,47.175332427485074,15.0,0,1,1,24,1331.9,32.8,35.8,0.6643356643356644,0.10243020698237862
+MNCA (tuned),11104.727954295966,0.5286179322462815,5977.187221364461,0.4814995680859316,0.7448490525012423,0.6406839211091188,0.12655502884420408,0.10326715793661159,78433.1838326286,30.191390941699975,16.01923076923077,0.18580999999999998,11212.920736551285,0.4423438310623169,6020.892835479641,0.4537708228509102,0.8177816514691232,0.7061138774961084,0.08668289690877401,0.05913687649157323,67721.7850080898,25.373417518222958,14.25,2,0,0,24,1325.6,30.3,30.3,0.6586538461538461,0.15586362718013336
+XGB (tuned + ensemble),1868.2429302564035,2.459977333362286,1131.3403541498637,3.0908212073715786,0.7784275988369416,0.7326751610286906,0.1521183469738161,0.10487866753851346,11151.77336671448,133.9681639809692,16.576923076923077,0.184395,1441.2213016748428,1.317580223083496,785.8975953484313,1.2809013038285242,0.8316341855574652,0.7341106406501618,0.1144861696628025,0.054690456125919155,11226.485943771408,74.70529109142498,13.5,0,0,2,24,1313.5,28.5,32.1,0.6459790209790209,0.08752950287187947
+CAT (default),173.12264615755814,0.2530022859573364,152.19981148640542,0.1746769705615129,0.7721520925382083,0.7508782741378199,0.14762383159906592,0.09673912909834526,503.5985284620293,11.812145121206303,16.75,0.18153,12.185660362243652,0.2613861560821533,5.736216485552211,0.13945330874004275,0.8708469956136012,0.8764795848440701,0.09472627265148731,0.048694739189140704,101.89620933712393,7.710770185087604,19.25,1,0,0,25,1308.5,29.6,31.7,0.6420454545454546,0.10408056855425275
+CAT (tuned),4861.749095384891,0.10756405500265268,3035.7630585976663,0.11792367197674246,0.7533425274831995,0.7418580085557809,0.14394572275280534,0.09786470048169181,26561.38119334446,7.02054598025311,17.192307692307693,0.179485,3259.4502482414246,0.05912292003631592,1394.4481643221718,0.05275041900206211,0.8504867849422679,0.7781645153943832,0.09050829480547845,0.05413571715045103,20607.594190900367,2.886943526422101,17.0,0,0,0,26,1299.2,41.1,27.8,0.631993006993007,0.07539691390572127
+GBM (tuned),1386.6633568268555,0.6239433655372033,855.5354588276103,0.6651164927916418,0.737683616453633,0.7596688264282848,0.15123309377717353,0.10299158861394081,10078.423717398238,42.45419816933193,17.634615384615383,0.18125,1317.1929507255554,0.32417428493499756,691.4641967411458,0.2643910314838089,0.7826548575969626,0.7981479775650631,0.08157588989660797,0.06910038121186715,9059.644744264673,20.34411810847653,18.5,0,1,0,25,1288.1,29.9,29.0,0.6219405594405595,0.08417253395400366
+MNCA (tuned + ensemble),11104.727954295966,11.626816547833956,5977.187221364461,9.077822725159232,0.6933896363411076,0.6660665157088731,0.1444822450466191,0.11574155496848519,78433.1838326286,625.8156905319904,17.653846153846153,0.196755,11212.920736551285,9.569536089897156,6020.892835479641,8.47783782257851,0.722368050936999,0.6622270828972778,0.09664772425546841,0.06590198674037494,67721.7850080898,462.66839224080115,15.75,0,0,2,24,1292.0,26.5,34.9,0.6215034965034965,0.10668541415880203
+XGB (tuned),1868.2429302564035,0.6344939470291138,1131.3403541498637,0.845858557757576,0.8187393857188691,0.7667685498472205,0.15449551107917392,0.10860550617326425,11151.77336671448,35.07178353015141,17.807692307692307,0.18658,1441.2213016748428,0.2507840394973755,785.8975953484313,0.21279594317419753,0.9017943832788667,0.7453177957281472,0.12200656150282413,0.06269228828209442,11226.485943771408,15.348416723363933,16.5,1,0,0,25,1289.8,29.4,25.3,0.618006993006993,0.10251976073586389
+TABM (default),26.77778566800631,0.20130640726823074,16.983704596070464,0.1991903141757489,0.7120650416184634,0.7086289666607134,0.1622519047697801,0.11661283756574645,212.38711719883958,11.49822311803021,18.153846153846153,0.18572,20.835053324699402,0.1561201810836792,11.598826273977398,0.14759862387674094,0.7417274447397321,0.7810887350534926,0.09137336521284833,0.06275561870896407,109.84095896574044,8.743598466674035,18.0,1,0,1,24,1281.0,29.5,24.9,0.6101398601398601,0.12406889112890061
+EBM (tuned + ensemble),2866.551095375648,0.3536289380146907,1716.6253639310917,0.31694700723366953,0.8144475987903695,0.7364009363908267,0.17180309869898544,0.13289820608093436,16005.580219570395,21.915829440477204,18.423076923076923,0.18934,1826.563945055008,0.30398499965667725,1142.3094021828929,0.2383204663966832,0.9368603191709515,0.9207311313375326,0.07547386841907339,0.05109125672741019,15335.47890204938,12.950523458443941,21.5,0,3,0,23,1276.6,36.7,28.2,0.6040209790209791,0.12124171082532008
+NN_TORCH (tuned + ensemble),8269.36125588417,3.6064914281551657,3548.397093003735,3.1069146452932506,0.8005534385996745,0.7867221786490863,0.15745609955540044,0.10096884964933617,54687.42205511106,206.78432079512532,19.884615384615383,0.186795,7021.069797754288,2.925987720489502,2792.139673756541,2.175346818589723,0.9508009219506811,0.917687560600783,0.0823266490663982,0.0717701052690009,44532.50472025028,168.68500762383525,20.0,0,0,1,25,1247.3,30.8,27.1,0.5708041958041958,0.07895497881555723
+REALMLP (tuned),17853.666671056013,0.2349767226439256,6069.468633023759,0.2967994896112342,0.7992732991174807,0.8048895624469216,0.16530717153664265,0.11169886093388302,114488.59335090281,15.66992951032636,20.76923076923077,0.179705,10652.850141763687,0.22168636322021484,5575.693006637695,0.23576453293502733,0.895201963654964,0.9670251309899789,0.1283441158885112,0.08568734793187416,88499.22644289241,12.792606929662101,20.5,0,1,0,25,1231.0,33.9,28.5,0.5506993006993007,0.07503622635930923
+EBM (tuned),2866.551095375648,0.04391795855302077,1716.6253639310917,0.042622423373582956,0.8796773591220505,0.7989370532749394,0.18031150115586747,0.13991074423102637,16005.580219570395,2.6200464262605005,20.865384615384617,0.19075999999999999,1826.563945055008,0.031216979026794434,1142.3094021828929,0.02993136666804911,1.0,1.0,0.08520888190451481,0.06958046400412875,15335.47890204938,1.3258183958395588,23.75,0,1,1,24,1227.1,36.6,31.5,0.548513986013986,0.08978323529643507
+EBM (default),11.305762657752403,0.07217896901644193,6.708906519026521,0.08642030214738769,0.8468070324259693,0.8110280220039858,0.18686874456979283,0.14250462388903842,76.4056328740172,4.809914969274192,22.23076923076923,0.19205,8.478393912315369,0.060648202896118164,5.571264059656597,0.07129895504487138,1.0,1.0,0.10192934818149862,0.06122058058323697,66.99828845613939,3.601780880785588,24.75,1,0,0,25,1206.9,28.3,34.8,0.5174825174825175,0.10315367705023558
+FASTAI (tuned + ensemble),2663.3156125178702,9.71036501114185,1626.2333617037298,10.35375213848753,0.7698173487890432,0.7888707552608327,0.19143489263209343,0.11424870338763603,20608.72361153851,575.9084247062348,22.326923076923077,0.18179499999999998,2361.516105532646,9.277374625205994,1350.0449662692658,6.569572430045136,0.9823907390988809,1.0,0.10018841132165368,0.07677125815183776,13001.178549428518,549.1440903510916,24.0,0,0,0,26,1202.1,28.7,33.1,0.5152972027972028,0.08032410389081089
+MNCA (default),29.761946293023918,0.45968284056736874,15.939524477151517,0.3666958147944243,0.8169283513779425,0.8227126311705688,0.19359838536157442,0.10940904253610519,187.22763978272167,23.803143758716253,22.826923076923077,0.19357,23.457369804382324,0.36945486068725586,13.667502577653629,0.33894040253174296,0.9808195818002508,0.9797952351969426,0.13240284359564197,0.08931940966654422,154.11579909730304,19.118442709424343,21.5,1,1,0,24,1187.8,28.8,27.9,0.5039335664335665,0.1019829541933546
+TABDPT (default),72.55373939184042,21.63674608560709,32.1753170626944,27.14316288922542,0.7421770023997575,0.7479465935897589,0.18444900328904643,0.12119989176698268,513.8148563269098,1550.6457261531152,23.48076923076923,0.20388,69.82611036300659,21.28597331047058,28.076698775708525,8.760817737139146,1.0,1.0,0.1080149448719816,0.07851722429401914,429.0853998300959,1143.1582415263433,29.5,1,3,0,22,1178.7,28.4,35.5,0.48907342657342656,0.14118801082755247
+REALMLP (default),110.65054762363434,0.2512630499326266,36.51262336985926,0.304026477962315,0.9170988508971886,0.8257233846816412,0.17480849590114966,0.10965875529591303,716.9172586317429,16.106317028225142,23.634615384615383,0.179885,61.38514173030853,0.19377470016479492,35.26610472383891,0.25260632993704635,1.0,1.0,0.10725973083151863,0.09589271240176966,562.1682174661271,13.084740916081461,26.25,1,0,0,25,1178.7,23.8,30.2,0.4855769230769231,0.09086138282523847
+NN_TORCH (tuned),8269.36125588417,0.1732868873156034,3548.397093003735,0.17387289280012472,0.9141063957909149,0.842287949515361,0.1775257283605692,0.11814031211601564,54687.42205511106,10.378521059679809,24.0,0.184685,7021.069797754288,0.11897110939025879,2792.139673756541,0.13285542411599796,1.0,1.0,0.10704543711737907,0.07835117203069997,44532.50472025028,7.793728816499673,25.0,0,0,0,26,1167.5,37.1,35.1,0.4772727272727273,0.055970780933021255
+XT (tuned + ensemble),723.008487197069,1.6600552705618052,480.9309873883464,1.918436680526551,0.8984673764067701,0.8698563213577479,0.20350469181458591,0.1535920856593196,5917.229990900821,105.19348400260608,24.884615384615383,0.188425,702.5076073408127,1.3369704484939575,378.34316304246136,1.3734038445009942,1.0,1.0,0.1315762721206637,0.09230140454586463,3777.3315625688056,84.73782880976256,28.0,0,0,0,26,1154.8,28.9,35.7,0.4571678321678322,0.059958692919405074
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+RF (tuned),911.769657428448,0.15133640399345985,531.6502580092293,0.19063529883897276,0.9772506936041817,0.9414350435106434,0.21876669694055456,0.18885525418574936,6577.080710276883,9.67186030508054,29.25,0.18775,798.9582680463791,0.13221728801727295,527.2734284753022,0.1275233993660041,1.0,1.0,0.16269556435701765,0.11206528012392108,5029.2193365439925,7.908293435451007,31.5,0,0,0,26,1064.0,29.3,35.0,0.35795454545454547,0.04504401000608687
+NN_TORCH (default),23.867143649321335,0.24185629991384652,13.87033788200038,0.29581404152824126,0.9698948249425869,0.9457939759123197,0.24766534415358382,0.1805126846920292,159.99336582070887,16.359564345871572,31.71153846153846,0.19833499999999998,19.20789933204651,0.18236374855041504,8.931817801759234,0.17241159209016133,1.0,1.0,0.1806992032072448,0.1282818632772945,162.04458259531137,9.195674192084631,33.0,0,0,0,26,1005.9,38.8,30.0,0.30201048951048953,0.03748733782316879
+FASTAI (default),10.92040595641503,0.607048539014963,5.789474679574097,0.6364629667404095,0.9807465319111015,0.9340218157343317,0.2458147993858494,0.18433902398032298,78.41470283692,37.58331718018816,32.15384615384615,0.19146000000000002,9.578287720680237,0.6503585577011108,4.679521651364521,0.5473457672031852,1.0,1.0,0.15595107304948763,0.13206479856027808,61.09577848128312,35.985779724786894,35.0,0,0,0,26,999.9,29.7,31.1,0.291958041958042,0.036046519543766274
+RF (default),1.1224354964036207,0.07520443659562331,0.5160590091828214,0.08504536046248315,0.9951894125278877,0.9662672985507942,0.2733008673941071,0.2607556340607851,7.699488729035172,4.8652515281017985,32.21153846153846,0.213715,0.8405723571777344,0.06134629249572754,0.44070860935344924,0.06492877134601137,1.0,1.0,0.22304594727380544,0.1387641342747614,5.263965350091326,4.042169058695611,35.75,0,0,0,26,1000.0,0.0,0.0,0.2906468531468531,0.04063169893049472
+XT (default),0.7937821791722224,0.076584870998676,0.45161836217627827,0.08730195757590078,0.9851460583683391,0.9723807339815378,0.2928638810006597,0.2925631030122461,5.4850410746785805,4.998284751448414,34.11538461538461,0.22224,0.7121238708496094,0.07150018215179443,0.3597485800421042,0.06793435113493712,1.0,1.0,0.21784002712442435,0.1545266336174043,3.777771179368423,4.378376796908203,38.0,0,0,0,26,949.4,35.6,31.6,0.24737762237762237,0.034356084606725006
+LR (tuned + ensemble),175.34701893879816,0.4311328667860765,114.81546568819135,0.35152858974096185,0.9447815704031849,0.9613573808489063,0.31304194938194396,0.27486950659356457,1394.89696800973,28.50704727597215,34.21153846153846,0.219115,157.3445065021515,0.20991504192352295,89.11614089252916,0.1966045265631563,1.0,1.0,0.260779813420035,0.18354757952857997,1033.8951605639145,9.91761243845308,37.5,1,0,0,25,946.4,32.2,34.1,0.24519230769230768,0.06657835051950235
+LR (tuned),175.34701893879816,0.13578540545243484,114.81546568819135,0.11644774040693313,0.9645430999513104,0.9706124256069096,0.3197153495116481,0.2866149774984877,1394.89696800973,6.834352988282761,34.92307692307692,0.21990500000000002,157.3445065021515,0.06698489189147949,89.11614089252916,0.08340922333653941,1.0,1.0,0.2839064101424172,0.1973963943493853,1033.8951605639145,4.726037355894221,38.75,0,0,1,25,925.0,34.9,43.3,0.229020979020979,0.04041420009021431
+LR (default),4.7979896068573,0.14980417031508225,2.784461238483506,0.15310137710602748,0.9745259999639975,0.9812456463551247,0.3329427914389331,0.32581993219914024,36.357934158034695,9.005429323015887,35.98076923076923,0.227395,4.980341196060181,0.0988316535949707,2.4349667711867333,0.10027059217560387,1.0,1.0,0.283906387231373,0.21846589850078024,26.549049340848278,5.855361941404224,39.0,0,0,0,26,891.8,35.4,28.6,0.20498251748251747,0.030292514887996096
+KNN (tuned + ensemble),16.958975911140442,0.2818152537712684,7.194345160625263,0.20399826229470497,1.0,0.9816350265387093,0.47672042775227436,0.5833708185909443,56.76087640724927,13.193730328032911,39.92307692307692,0.297435,6.915013313293457,0.11628735065460205,3.5025132784274384,0.1592449370444793,1.0,1.0,0.4130987117462378,0.6739246116612398,52.78507887384432,10.328630925855668,43.0,0,0,0,26,745.5,44.9,30.5,0.11538461538461539,0.030206633151293592
+KNN (tuned),16.958975911140442,0.05680100734417255,7.194345160625263,0.04235176068048229,1.0,0.9774922422312161,0.5019983015456773,0.6443397962703555,56.76087640724927,2.623837177847182,41.21153846153846,0.301285,6.915013313293457,0.03549385070800781,3.5025132784274384,0.03322185201916636,1.0,1.0,0.5143946348760557,0.7015107115204589,52.78507887384432,1.9257403822827532,44.0,0,0,1,25,676.5,41.9,47.1,0.08610139860139861,0.035393141891706566
+KNN (default),0.30434812949253964,0.03149266426379864,0.14343197725298135,0.03163346219935531,1.0,1.0,0.5949762374279601,0.9484018378342699,1.002692426657382,1.6405188884998085,44.26923076923077,0.34604,0.17687928676605225,0.01871800422668457,0.0745054444441075,0.021103033819676938,1.0,1.0,0.6151111085811185,1.0,1.0,1.0188814221661033,45.0,0,0,0,26,383.7,60.2,115.9,0.016608391608391608,0.02263422903807308
diff --git a/data/lite/tabpfn-tabicl-cls/tuning-impact-elo-horizontal.pdf b/data/lite/tabpfn-tabicl-cls/tuning-impact-elo-horizontal.pdf
new file mode 100644
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diff --git a/data/lite/tabpfn-tabicl-cls/tuning-impact-elo-horizontal.png.zip b/data/lite/tabpfn-tabicl-cls/tuning-impact-elo-horizontal.png.zip
new file mode 100644
index 0000000000000000000000000000000000000000..11f6de74fa20ca51951d5e65c8838b9e75acc2bf
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+++ b/data/lite/tabpfn-tabicl-cls/tuning-impact-elo-horizontal.png.zip
@@ -0,0 +1,3 @@
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+size 138602
diff --git a/data/lite/tabpfn-tabicl/figures/critical-diagram.pdf b/data/lite/tabpfn-tabicl/figures/critical-diagram.pdf
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+version https://git-lfs.github.com/spec/v1
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diff --git a/data/lite/tabpfn-tabicl/leaderboard.tex b/data/lite/tabpfn-tabicl/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..470e5847143808188c07aa37c1d5e5b76d097272
--- /dev/null
+++ b/data/lite/tabpfn-tabicl/leaderboard.tex
@@ -0,0 +1,53 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+TabPFNv2 (T+E) & \textcolor{gold}{\textbf{1507${}_{-45,+32}$}} & \textcolor{gold}{\textbf{0.700}} & \textcolor{gold}{\textbf{8.5}} & \textcolor{gold}{\textbf{2.8}} & \textcolor{gold}{\textbf{6}} & \textcolor{gold}{\textbf{8.0\%}} & 3361.32 & 51.67 \\
+AutoGluon 1.3 (4h) & \textcolor{silver}{\textbf{1434${}_{-31,+33}$}} & 0.440 & \textcolor{silver}{\textbf{11.0}} & 6.4 & 1 & \textcolor{silver}{\textbf{10.4\%}} & 2085.73 & 2.16 \\
+RealMLP (T+E) & \textcolor{bronze}{\textbf{1408${}_{-30,+28}$}} & 0.402 & \textcolor{bronze}{\textbf{12.1}} & 6.9 & 0 & 12.3\% & 5575.69 & 4.58 \\
+TabPFNv2 (T) & 1402${}_{-33,+32}$ & \textcolor{silver}{\textbf{0.544}} & 12.2 & \textcolor{bronze}{\textbf{4.5}} & 1 & 10.6\% & 3361.32 & 0.53 \\
+TabM (T+E) & 1392${}_{-40,+33}$ & 0.483 & 12.8 & 6.2 & 1 & 12.0\% & 2899.02 & 1.46 \\
+TabICL (D) & 1378${}_{-31,+37}$ & \textcolor{bronze}{\textbf{0.543}} & 13.4 & \textcolor{silver}{\textbf{3.9}} & \textcolor{silver}{\textbf{4}} & \textcolor{bronze}{\textbf{10.5\%}} & 9.07 & 2.01 \\
+LightGBM (T+E) & 1349${}_{-33,+29}$ & 0.319 & 14.8 & 11.6 & 0 & 14.1\% & 691.46 & 1.46 \\
+TabPFNv2 (D) & 1334${}_{-35,+35}$ & 0.499 & 15.5 & \textcolor{bronze}{\textbf{4.5}} & \textcolor{bronze}{\textbf{3}} & 12.1\% & 4.28 & 0.51 \\
+CatBoost (T+E) & 1332${}_{-36,+33}$ & 0.271 & 15.8 & 9.8 & 0 & 13.5\% & 1394.45 & 0.62 \\
+TabM (T) & 1330${}_{-31,+33}$ & 0.387 & 15.7 & 7.7 & 0 & 13.1\% & 2899.02 & 0.17 \\
+ModernNCA (T) & 1326${}_{-31,+31}$ & 0.255 & 16.0 & 6.4 & 2 & 12.7\% & 6020.89 & 0.45 \\
+XGBoost (T+E) & 1314${}_{-33,+29}$ & 0.222 & 16.6 & 11.4 & 0 & 15.2\% & 785.90 & 1.28 \\
+CatBoost (D) & 1308${}_{-32,+30}$ & 0.228 & 16.8 & 9.6 & 1 & 14.8\% & 5.74 & 0.14 \\
+CatBoost (T) & 1299${}_{-28,+42}$ & 0.247 & 17.2 & 13.3 & 0 & 14.4\% & 1394.45 & 0.05 \\
+ModernNCA (T+E) & 1292${}_{-35,+27}$ & 0.307 & 17.7 & 9.4 & 0 & 14.4\% & 6020.89 & 8.48 \\
+XGBoost (T) & 1290${}_{-26,+30}$ & 0.181 & 17.8 & 9.8 & 1 & 15.4\% & 785.90 & 0.21 \\
+LightGBM (T) & 1288${}_{-29,+30}$ & 0.262 & 17.6 & 11.9 & 0 & 15.1\% & 691.46 & 0.26 \\
+TabM (D) & 1281${}_{-25,+30}$ & 0.288 & 18.2 & 8.1 & 1 & 16.2\% & 11.60 & 0.15 \\
+EBM (T+E) & 1277${}_{-29,+37}$ & 0.186 & 18.4 & 8.2 & 0 & 17.2\% & 1142.31 & 0.24 \\
+TorchMLP (T+E) & 1247${}_{-28,+31}$ & 0.199 & 19.9 & 12.7 & 0 & 15.7\% & 2792.14 & 2.18 \\
+RealMLP (T) & 1231${}_{-29,+34}$ & 0.201 & 20.8 & 13.3 & 0 & 16.5\% & 5575.69 & 0.24 \\
+EBM (T) & 1227${}_{-32,+37}$ & 0.120 & 20.9 & 11.1 & 0 & 18.0\% & 1142.31 & 0.03 \\
+EBM (D) & 1207${}_{-35,+29}$ & 0.153 & 22.2 & 9.7 & 1 & 18.7\% & 5.57 & 0.07 \\
+FastaiMLP (T+E) & 1202${}_{-34,+29}$ & 0.230 & 22.3 & 12.4 & 0 & 19.1\% & 1350.04 & 6.57 \\
+ModernNCA (D) & 1188${}_{-28,+29}$ & 0.183 & 22.8 & 9.8 & 1 & 19.4\% & 13.67 & 0.34 \\
+TabDPT (D) & 1179${}_{-36,+29}$ & 0.258 & 23.5 & 7.1 & 1 & 18.4\% & 28.08 & 8.76 \\
+RealMLP (D) & 1179${}_{-31,+24}$ & 0.083 & 23.6 & 11.0 & 1 & 17.5\% & 35.27 & 0.25 \\
+TorchMLP (T) & 1168${}_{-36,+38}$ & 0.086 & 24.0 & 17.9 & 0 & 17.8\% & 2792.14 & 0.13 \\
+ExtraTrees (T+E) & 1155${}_{-36,+29}$ & 0.102 & 24.9 & 16.7 & 0 & 20.4\% & 378.34 & 1.37 \\
+FastaiMLP (T) & 1140${}_{-32,+30}$ & 0.113 & 25.5 & 14.7 & 0 & 20.7\% & 1350.04 & 0.61 \\
+XGBoost (D) & 1130${}_{-32,+28}$ & 0.032 & 26.0 & 21.9 & 0 & 19.1\% & 2.66 & 0.17 \\
+ExtraTrees (T) & 1128${}_{-26,+31}$ & 0.072 & 26.0 & 13.8 & 0 & 20.6\% & 378.34 & 0.15 \\
+RandomForest (T+E) & 1114${}_{-34,+28}$ & 0.057 & 27.0 & 14.8 & 0 & 21.0\% & 527.27 & 1.16 \\
+LightGBM (D) & 1098${}_{-30,+32}$ & 0.030 & 27.6 & 24.0 & 0 & 20.1\% & 3.01 & 0.18 \\
+RandomForest (T) & 1064${}_{-35,+30}$ & 0.023 & 29.2 & 22.2 & 0 & 21.9\% & 527.27 & 0.13 \\
+TorchMLP (D) & 1006${}_{-30,+39}$ & 0.030 & 31.7 & 26.7 & 0 & 24.8\% & 8.93 & 0.17 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.005 & 32.2 & 24.6 & 0 & 27.3\% & 0.44 & 0.06 \\
+FastaiMLP (D) & 1000${}_{-32,+30}$ & 0.019 & 32.2 & 27.7 & 0 & 24.6\% & 4.68 & 0.55 \\
+ExtraTrees (D) & 949${}_{-32,+36}$ & 0.015 & 34.1 & 29.1 & 0 & 29.3\% & 0.36 & 0.07 \\
+Linear (T+E) & 946${}_{-35,+33}$ & 0.055 & 34.2 & 15.0 & 1 & 31.3\% & 89.12 & 0.20 \\
+Linear (T) & 925${}_{-44,+35}$ & 0.035 & 34.9 & 24.7 & 0 & 32.0\% & 89.12 & 0.08 \\
+Linear (D) & 892${}_{-29,+36}$ & 0.025 & 36.0 & 33.0 & 0 & 33.3\% & 2.43 & 0.10 \\
+KNN (T+E) & 746${}_{-31,+45}$ & 0.000 & 39.9 & 33.1 & 0 & 47.7\% & 3.50 & 0.16 \\
+KNN (T) & 676${}_{-48,+42}$ & 0.000 & 41.2 & 28.3 & 0 & 50.2\% & 3.50 & 0.03 \\
+KNN (D) & 384${}_{-116,+61}$ & 0.000 & 44.3 & 44.2 & 0 & 59.5\% & 0.07 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/lite/tabpfn-tabicl/tabarena_leaderboard.csv b/data/lite/tabpfn-tabicl/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..df059e4121634d1f0f2b668e430505bc14507b44
--- /dev/null
+++ b/data/lite/tabpfn-tabicl/tabarena_leaderboard.csv
@@ -0,0 +1,46 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+TABPFNV2 (tuned + ensemble),14624.10810300937,166.6936253951146,3837.093570770289,71.22922446033486,0.2999799624009034,0.37900004959687766,0.08031051995518385,0.05455935044977521,73712.17169915051,6326.444952096131,8.461538461538462,0.1749,8447.90291428566,59.9476752281189,3361.317280362242,51.668880171806535,0.21441671773452015,0.3480554663987826,0.03548854933740464,0.027745461982937078,32774.70384527536,4094.471326370238,5.5,6,1,3,16,1506.7,31.8,44.7,0.8304195804195804,0.358094093982103
+AutoGluon 1.3 (4h),5811.540137483524,3.0737958137805643,3715.2599271484114,3.044362316385498,0.5599854226187498,0.5617828950350577,0.10445421398736063,0.0599680756233326,37983.87924037632,165.48830983683717,11.01923076923077,0.17751,5213.819254875183,2.0895845890045166,2085.7270446537464,2.161349128608518,0.5600808158121042,0.5574000428410242,0.06078825021132733,0.06659288302785793,30345.10100676535,118.3021811054546,9.0,1,0,1,24,1433.5,32.8,30.7,0.7722902097902098,0.15518544636191695
+REALMLP (tuned + ensemble),17853.666671056013,5.060677555891184,6069.468633023759,5.352972929535142,0.5983187605703805,0.5697719962134024,0.1225244655490201,0.0697158337547381,114488.59335090281,312.9299756303972,12.115384615384615,0.180765,10652.850141763687,4.7910391092300415,5575.693006637695,4.577857176513236,0.5696681015084324,0.5408431020221902,0.06341551053522204,0.04576778429435464,88499.22644289241,286.2007603461078,11.5,0,1,2,23,1407.7,28.0,30.0,0.7473776223776224,0.1450476689017345
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+TABM (tuned + ensemble),7183.65333878994,2.169150572556716,3570.712955098323,1.861754182648195,0.5166525238887076,0.5534887980632361,0.11956349986158774,0.08309123144106954,42989.483218042995,121.47528176372664,12.846153846153847,0.18310500000000002,6058.230099320412,1.6269241571426392,2899.0220559668596,1.460880880345262,0.47136645123500964,0.5213443194889227,0.07336602828104005,0.040297742432766875,37327.19260107514,109.27107326295308,9.5,1,0,1,24,1392.2,32.7,39.4,0.7307692307692307,0.16156485134544094
+TABICL (default),21.54241045621725,2.6437206268310547,10.219926189931016,1.9528462364644343,0.45732829959408833,0.5557361534084676,0.10499055070178752,0.06310686388674132,140.84761892371782,137.0823191069963,13.365384615384615,0.177865,18.648964643478394,2.1483960151672363,9.073519792448254,2.007502492806867,0.46959481282562443,0.6652418475567421,0.05302697309380272,0.028777901101857722,117.57303754113096,107.76328227971135,13.5,4,2,1,19,1378.5,36.4,30.5,0.7189685314685315,0.2556026489858902
+GBM (tuned + ensemble),1386.6633568268555,2.647538212629465,855.5354588276103,2.6861264004396594,0.6806482110513491,0.7106186510085695,0.141362130997881,0.08782930779014528,10078.423717398238,182.57724180654643,14.788461538461538,0.18084,1317.1929507255554,2.008246660232544,691.4641967411458,1.4553717527055694,0.7005535661954161,0.7652961068739543,0.07965351598262033,0.06332006129013433,9059.644744264673,90.72358893879789,14.75,0,0,1,25,1349.4,28.6,33.0,0.6866258741258742,0.08652272628643233
+TABPFNV2 (default),12.006092300781837,1.2122490681134737,5.3148066442099084,0.6434373636958165,0.5013642769155608,0.6076681115357273,0.12052806276021125,0.08926430649670257,74.57934257049592,50.66248833495304,15.538461538461538,0.194575,9.273962259292603,0.4255213737487793,4.283948847917442,0.5110342254172261,0.3771347187714357,0.8078260737500734,0.07359963710195983,0.07618105155863322,62.366726819804335,28.80821414198816,14.5,3,2,2,19,1333.5,34.9,34.7,0.6695804195804196,0.22455622157365326
+TABM (tuned),7183.65333878994,0.2019361532651461,3570.712955098323,0.2168923191387062,0.6127855914191946,0.6153990479247885,0.130856425990661,0.09537399493661194,42989.483218042995,13.062348029072668,15.692307692307692,0.18589499999999998,6058.230099320412,0.1839456558227539,2899.0220559668596,0.171251855223312,0.5915500043256848,0.6288985041617685,0.09148128983327741,0.05588464476989181,37327.19260107514,9.17800659262771,12.0,0,2,2,22,1329.7,32.2,30.3,0.666083916083916,0.13000506341067208
+CAT (tuned + ensemble),4861.749095384891,1.0365588940106905,3035.7630585976663,0.9271931106606893,0.728920075896456,0.6786642048762535,0.13487992339677388,0.0909217961956513,26561.38119334446,53.29220521181529,15.76923076923077,0.178855,3259.4502482414246,0.9432240724563599,1394.4481643221718,0.6180738220288715,0.7992439551459185,0.7007978686240202,0.07405507598059591,0.051124014208117546,20607.594190900367,47.175332427485074,15.0,0,1,1,24,1331.9,32.8,35.8,0.6643356643356644,0.10243020698237862
+MNCA (tuned),11104.727954295966,0.5286179322462815,5977.187221364461,0.4814995680859316,0.7448490525012423,0.6406839211091188,0.12655502884420408,0.10326715793661159,78433.1838326286,30.191390941699975,16.01923076923077,0.18580999999999998,11212.920736551285,0.4423438310623169,6020.892835479641,0.4537708228509102,0.8177816514691232,0.7061138774961084,0.08668289690877401,0.05913687649157323,67721.7850080898,25.373417518222958,14.25,2,0,0,24,1325.6,30.3,30.3,0.6586538461538461,0.15586362718013336
+XGB (tuned + ensemble),1868.2429302564035,2.459977333362286,1131.3403541498637,3.0908212073715786,0.7784275988369416,0.7326751610286906,0.1521183469738161,0.10487866753851346,11151.77336671448,133.9681639809692,16.576923076923077,0.184395,1441.2213016748428,1.317580223083496,785.8975953484313,1.2809013038285242,0.8316341855574652,0.7341106406501618,0.1144861696628025,0.054690456125919155,11226.485943771408,74.70529109142498,13.5,0,0,2,24,1313.5,28.5,32.1,0.6459790209790209,0.08752950287187947
+CAT (default),173.12264615755814,0.2530022859573364,152.19981148640542,0.1746769705615129,0.7721520925382083,0.7508782741378199,0.14762383159906592,0.09673912909834526,503.5985284620293,11.812145121206303,16.75,0.18153,12.185660362243652,0.2613861560821533,5.736216485552211,0.13945330874004275,0.8708469956136012,0.8764795848440701,0.09472627265148731,0.048694739189140704,101.89620933712393,7.710770185087604,19.25,1,0,0,25,1308.5,29.6,31.7,0.6420454545454546,0.10408056855425275
+CAT (tuned),4861.749095384891,0.10756405500265268,3035.7630585976663,0.11792367197674246,0.7533425274831995,0.7418580085557809,0.14394572275280534,0.09786470048169181,26561.38119334446,7.02054598025311,17.192307692307693,0.179485,3259.4502482414246,0.05912292003631592,1394.4481643221718,0.05275041900206211,0.8504867849422679,0.7781645153943832,0.09050829480547845,0.05413571715045103,20607.594190900367,2.886943526422101,17.0,0,0,0,26,1299.2,41.1,27.8,0.631993006993007,0.07539691390572127
+GBM (tuned),1386.6633568268555,0.6239433655372033,855.5354588276103,0.6651164927916418,0.737683616453633,0.7596688264282848,0.15123309377717353,0.10299158861394081,10078.423717398238,42.45419816933193,17.634615384615383,0.18125,1317.1929507255554,0.32417428493499756,691.4641967411458,0.2643910314838089,0.7826548575969626,0.7981479775650631,0.08157588989660797,0.06910038121186715,9059.644744264673,20.34411810847653,18.5,0,1,0,25,1288.1,29.9,29.0,0.6219405594405595,0.08417253395400366
+MNCA (tuned + ensemble),11104.727954295966,11.626816547833956,5977.187221364461,9.077822725159232,0.6933896363411076,0.6660665157088731,0.1444822450466191,0.11574155496848519,78433.1838326286,625.8156905319904,17.653846153846153,0.196755,11212.920736551285,9.569536089897156,6020.892835479641,8.47783782257851,0.722368050936999,0.6622270828972778,0.09664772425546841,0.06590198674037494,67721.7850080898,462.66839224080115,15.75,0,0,2,24,1292.0,26.5,34.9,0.6215034965034965,0.10668541415880203
+XGB (tuned),1868.2429302564035,0.6344939470291138,1131.3403541498637,0.845858557757576,0.8187393857188691,0.7667685498472205,0.15449551107917392,0.10860550617326425,11151.77336671448,35.07178353015141,17.807692307692307,0.18658,1441.2213016748428,0.2507840394973755,785.8975953484313,0.21279594317419753,0.9017943832788667,0.7453177957281472,0.12200656150282413,0.06269228828209442,11226.485943771408,15.348416723363933,16.5,1,0,0,25,1289.8,29.4,25.3,0.618006993006993,0.10251976073586389
+TABM (default),26.77778566800631,0.20130640726823074,16.983704596070464,0.1991903141757489,0.7120650416184634,0.7086289666607134,0.1622519047697801,0.11661283756574645,212.38711719883958,11.49822311803021,18.153846153846153,0.18572,20.835053324699402,0.1561201810836792,11.598826273977398,0.14759862387674094,0.7417274447397321,0.7810887350534926,0.09137336521284833,0.06275561870896407,109.84095896574044,8.743598466674035,18.0,1,0,1,24,1281.0,29.5,24.9,0.6101398601398601,0.12406889112890061
+EBM (tuned + ensemble),2866.551095375648,0.3536289380146907,1716.6253639310917,0.31694700723366953,0.8144475987903695,0.7364009363908267,0.17180309869898544,0.13289820608093436,16005.580219570395,21.915829440477204,18.423076923076923,0.18934,1826.563945055008,0.30398499965667725,1142.3094021828929,0.2383204663966832,0.9368603191709515,0.9207311313375326,0.07547386841907339,0.05109125672741019,15335.47890204938,12.950523458443941,21.5,0,3,0,23,1276.6,36.7,28.2,0.6040209790209791,0.12124171082532008
+NN_TORCH (tuned + ensemble),8269.36125588417,3.6064914281551657,3548.397093003735,3.1069146452932506,0.8005534385996745,0.7867221786490863,0.15745609955540044,0.10096884964933617,54687.42205511106,206.78432079512532,19.884615384615383,0.186795,7021.069797754288,2.925987720489502,2792.139673756541,2.175346818589723,0.9508009219506811,0.917687560600783,0.0823266490663982,0.0717701052690009,44532.50472025028,168.68500762383525,20.0,0,0,1,25,1247.3,30.8,27.1,0.5708041958041958,0.07895497881555723
+REALMLP (tuned),17853.666671056013,0.2349767226439256,6069.468633023759,0.2967994896112342,0.7992732991174807,0.8048895624469216,0.16530717153664265,0.11169886093388302,114488.59335090281,15.66992951032636,20.76923076923077,0.179705,10652.850141763687,0.22168636322021484,5575.693006637695,0.23576453293502733,0.895201963654964,0.9670251309899789,0.1283441158885112,0.08568734793187416,88499.22644289241,12.792606929662101,20.5,0,1,0,25,1231.0,33.9,28.5,0.5506993006993007,0.07503622635930923
+EBM (tuned),2866.551095375648,0.04391795855302077,1716.6253639310917,0.042622423373582956,0.8796773591220505,0.7989370532749394,0.18031150115586747,0.13991074423102637,16005.580219570395,2.6200464262605005,20.865384615384617,0.19075999999999999,1826.563945055008,0.031216979026794434,1142.3094021828929,0.02993136666804911,1.0,1.0,0.08520888190451481,0.06958046400412875,15335.47890204938,1.3258183958395588,23.75,0,1,1,24,1227.1,36.6,31.5,0.548513986013986,0.08978323529643507
+EBM (default),11.305762657752403,0.07217896901644193,6.708906519026521,0.08642030214738769,0.8468070324259693,0.8110280220039858,0.18686874456979283,0.14250462388903842,76.4056328740172,4.809914969274192,22.23076923076923,0.19205,8.478393912315369,0.060648202896118164,5.571264059656597,0.07129895504487138,1.0,1.0,0.10192934818149862,0.06122058058323697,66.99828845613939,3.601780880785588,24.75,1,0,0,25,1206.9,28.3,34.8,0.5174825174825175,0.10315367705023558
+FASTAI (tuned + ensemble),2663.3156125178702,9.71036501114185,1626.2333617037298,10.35375213848753,0.7698173487890432,0.7888707552608327,0.19143489263209343,0.11424870338763603,20608.72361153851,575.9084247062348,22.326923076923077,0.18179499999999998,2361.516105532646,9.277374625205994,1350.0449662692658,6.569572430045136,0.9823907390988809,1.0,0.10018841132165368,0.07677125815183776,13001.178549428518,549.1440903510916,24.0,0,0,0,26,1202.1,28.7,33.1,0.5152972027972028,0.08032410389081089
+MNCA (default),29.761946293023918,0.45968284056736874,15.939524477151517,0.3666958147944243,0.8169283513779425,0.8227126311705688,0.19359838536157442,0.10940904253610519,187.22763978272167,23.803143758716253,22.826923076923077,0.19357,23.457369804382324,0.36945486068725586,13.667502577653629,0.33894040253174296,0.9808195818002508,0.9797952351969426,0.13240284359564197,0.08931940966654422,154.11579909730304,19.118442709424343,21.5,1,1,0,24,1187.8,28.8,27.9,0.5039335664335665,0.1019829541933546
+TABDPT (default),72.55373939184042,21.63674608560709,32.1753170626944,27.14316288922542,0.7421770023997575,0.7479465935897589,0.18444900328904643,0.12119989176698268,513.8148563269098,1550.6457261531152,23.48076923076923,0.20388,69.82611036300659,21.28597331047058,28.076698775708525,8.760817737139146,1.0,1.0,0.1080149448719816,0.07851722429401914,429.0853998300959,1143.1582415263433,29.5,1,3,0,22,1178.7,28.4,35.5,0.48907342657342656,0.14118801082755247
+REALMLP (default),110.65054762363434,0.2512630499326266,36.51262336985926,0.304026477962315,0.9170988508971886,0.8257233846816412,0.17480849590114966,0.10965875529591303,716.9172586317429,16.106317028225142,23.634615384615383,0.179885,61.38514173030853,0.19377470016479492,35.26610472383891,0.25260632993704635,1.0,1.0,0.10725973083151863,0.09589271240176966,562.1682174661271,13.084740916081461,26.25,1,0,0,25,1178.7,23.8,30.2,0.4855769230769231,0.09086138282523847
+NN_TORCH (tuned),8269.36125588417,0.1732868873156034,3548.397093003735,0.17387289280012472,0.9141063957909149,0.842287949515361,0.1775257283605692,0.11814031211601564,54687.42205511106,10.378521059679809,24.0,0.184685,7021.069797754288,0.11897110939025879,2792.139673756541,0.13285542411599796,1.0,1.0,0.10704543711737907,0.07835117203069997,44532.50472025028,7.793728816499673,25.0,0,0,0,26,1167.5,37.1,35.1,0.4772727272727273,0.055970780933021255
+XT (tuned + ensemble),723.008487197069,1.6600552705618052,480.9309873883464,1.918436680526551,0.8984673764067701,0.8698563213577479,0.20350469181458591,0.1535920856593196,5917.229990900821,105.19348400260608,24.884615384615383,0.188425,702.5076073408127,1.3369704484939575,378.34316304246136,1.3734038445009942,1.0,1.0,0.1315762721206637,0.09230140454586463,3777.3315625688056,84.73782880976256,28.0,0,0,0,26,1154.8,28.9,35.7,0.4571678321678322,0.059958692919405074
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+RF (default),1.1224354964036207,0.07520443659562331,0.5160590091828214,0.08504536046248315,0.9951894125278877,0.9662672985507942,0.2733008673941071,0.2607556340607851,7.699488729035172,4.8652515281017985,32.21153846153846,0.213715,0.8405723571777344,0.06134629249572754,0.44070860935344924,0.06492877134601137,1.0,1.0,0.22304594727380544,0.1387641342747614,5.263965350091326,4.042169058695611,35.75,0,0,0,26,1000.0,0.0,0.0,0.2906468531468531,0.04063169893049472
+XT (default),0.7937821791722224,0.076584870998676,0.45161836217627827,0.08730195757590078,0.9851460583683391,0.9723807339815378,0.2928638810006597,0.2925631030122461,5.4850410746785805,4.998284751448414,34.11538461538461,0.22224,0.7121238708496094,0.07150018215179443,0.3597485800421042,0.06793435113493712,1.0,1.0,0.21784002712442435,0.1545266336174043,3.777771179368423,4.378376796908203,38.0,0,0,0,26,949.4,35.6,31.6,0.24737762237762237,0.034356084606725006
+LR (tuned + ensemble),175.34701893879816,0.4311328667860765,114.81546568819135,0.35152858974096185,0.9447815704031849,0.9613573808489063,0.31304194938194396,0.27486950659356457,1394.89696800973,28.50704727597215,34.21153846153846,0.219115,157.3445065021515,0.20991504192352295,89.11614089252916,0.1966045265631563,1.0,1.0,0.260779813420035,0.18354757952857997,1033.8951605639145,9.91761243845308,37.5,1,0,0,25,946.4,32.2,34.1,0.24519230769230768,0.06657835051950235
+LR (tuned),175.34701893879816,0.13578540545243484,114.81546568819135,0.11644774040693313,0.9645430999513104,0.9706124256069096,0.3197153495116481,0.2866149774984877,1394.89696800973,6.834352988282761,34.92307692307692,0.21990500000000002,157.3445065021515,0.06698489189147949,89.11614089252916,0.08340922333653941,1.0,1.0,0.2839064101424172,0.1973963943493853,1033.8951605639145,4.726037355894221,38.75,0,0,1,25,925.0,34.9,43.3,0.229020979020979,0.04041420009021431
+LR (default),4.7979896068573,0.14980417031508225,2.784461238483506,0.15310137710602748,0.9745259999639975,0.9812456463551247,0.3329427914389331,0.32581993219914024,36.357934158034695,9.005429323015887,35.98076923076923,0.227395,4.980341196060181,0.0988316535949707,2.4349667711867333,0.10027059217560387,1.0,1.0,0.283906387231373,0.21846589850078024,26.549049340848278,5.855361941404224,39.0,0,0,0,26,891.8,35.4,28.6,0.20498251748251747,0.030292514887996096
+KNN (tuned + ensemble),16.958975911140442,0.2818152537712684,7.194345160625263,0.20399826229470497,1.0,0.9816350265387093,0.47672042775227436,0.5833708185909443,56.76087640724927,13.193730328032911,39.92307692307692,0.297435,6.915013313293457,0.11628735065460205,3.5025132784274384,0.1592449370444793,1.0,1.0,0.4130987117462378,0.6739246116612398,52.78507887384432,10.328630925855668,43.0,0,0,0,26,745.5,44.9,30.5,0.11538461538461539,0.030206633151293592
+KNN (tuned),16.958975911140442,0.05680100734417255,7.194345160625263,0.04235176068048229,1.0,0.9774922422312161,0.5019983015456773,0.6443397962703555,56.76087640724927,2.623837177847182,41.21153846153846,0.301285,6.915013313293457,0.03549385070800781,3.5025132784274384,0.03322185201916636,1.0,1.0,0.5143946348760557,0.7015107115204589,52.78507887384432,1.9257403822827532,44.0,0,0,1,25,676.5,41.9,47.1,0.08610139860139861,0.035393141891706566
+KNN (default),0.30434812949253964,0.03149266426379864,0.14343197725298135,0.03163346219935531,1.0,1.0,0.5949762374279601,0.9484018378342699,1.002692426657382,1.6405188884998085,44.26923076923077,0.34604,0.17687928676605225,0.01871800422668457,0.0745054444441075,0.021103033819676938,1.0,1.0,0.6151111085811185,1.0,1.0,1.0188814221661033,45.0,0,0,0,26,383.7,60.2,115.9,0.016608391608391608,0.02263422903807308
diff --git a/data/lite/tabpfn-tabicl/tuning-impact-elo-horizontal.pdf b/data/lite/tabpfn-tabicl/tuning-impact-elo-horizontal.pdf
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diff --git a/data/lite/tabpfn-tabicl/tuning-impact-elo-horizontal.png.zip b/data/lite/tabpfn-tabicl/tuning-impact-elo-horizontal.png.zip
new file mode 100644
index 0000000000000000000000000000000000000000..11f6de74fa20ca51951d5e65c8838b9e75acc2bf
--- /dev/null
+++ b/data/lite/tabpfn-tabicl/tuning-impact-elo-horizontal.png.zip
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4e3537446452cbef6ee98d645ac7ca084376854b8328e0703c07f9a66eb7f233
+size 138602
diff --git a/data/lite/tabpfn/figures/critical-diagram.pdf b/data/lite/tabpfn/figures/critical-diagram.pdf
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index 0000000000000000000000000000000000000000..cbd8c146f2380f529762ed99d807e44ba4a91a75
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+++ b/data/lite/tabpfn/figures/critical-diagram.png.zip
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+version https://git-lfs.github.com/spec/v1
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diff --git a/data/lite/tabpfn/leaderboard.tex b/data/lite/tabpfn/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..cacdd671405bca1878d8b988f07657a6572aaf5d
--- /dev/null
+++ b/data/lite/tabpfn/leaderboard.tex
@@ -0,0 +1,52 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+TabPFNv2 (T+E) & \textcolor{gold}{\textbf{1532${}_{-28,+31}$}} & \textcolor{gold}{\textbf{0.712}} & \textcolor{gold}{\textbf{7.8}} & \textcolor{gold}{\textbf{2.2}} & \textcolor{gold}{\textbf{11}} & \textcolor{gold}{\textbf{6.1\%}} & 3650.56 & 52.33 \\
+AutoGluon 1.3 (4h) & \textcolor{silver}{\textbf{1475${}_{-28,+36}$}} & \textcolor{bronze}{\textbf{0.493}} & \textcolor{silver}{\textbf{9.7}} & 5.5 & 1 & \textcolor{bronze}{\textbf{8.9\%}} & 2518.14 & 2.33 \\
+RealMLP (T+E) & \textcolor{bronze}{\textbf{1454${}_{-35,+29}$}} & 0.477 & \textcolor{bronze}{\textbf{10.6}} & 5.8 & 1 & 10.1\% & 6142.88 & 4.52 \\
+TabPFNv2 (T) & 1431${}_{-37,+26}$ & \textcolor{silver}{\textbf{0.540}} & 11.6 & \textcolor{silver}{\textbf{4.2}} & 1 & \textcolor{silver}{\textbf{8.8\%}} & 3650.56 & 0.54 \\
+TabM (T+E) & 1405${}_{-26,+36}$ & 0.484 & 12.4 & 6.5 & 1 & 10.1\% & 3407.57 & 1.45 \\
+LightGBM (T+E) & 1386${}_{-31,+27}$ & 0.361 & 13.4 & 10.6 & 0 & 12.4\% & 780.12 & 2.13 \\
+CatBoost (T+E) & 1374${}_{-32,+26}$ & 0.342 & 14.0 & 9.3 & 0 & 11.7\% & 2234.83 & 0.65 \\
+TabPFNv2 (D) & 1348${}_{-24,+30}$ & 0.483 & 15.1 & \textcolor{bronze}{\textbf{4.3}} & \textcolor{bronze}{\textbf{3}} & 10.4\% & 4.97 & 0.55 \\
+TabM (T) & 1345${}_{-36,+26}$ & 0.382 & 15.3 & 8.2 & 0 & 11.2\% & 3407.57 & 0.17 \\
+CatBoost (T) & 1342${}_{-27,+27}$ & 0.316 & 15.3 & 10.2 & 0 & 12.4\% & 2234.83 & 0.07 \\
+XGBoost (T+E) & 1335${}_{-29,+22}$ & 0.271 & 15.8 & 11.6 & 0 & 13.4\% & 848.99 & 1.87 \\
+CatBoost (D) & 1327${}_{-27,+34}$ & 0.259 & 16.1 & 9.7 & 1 & 13.4\% & 8.13 & 0.13 \\
+ModernNCA (T+E) & 1326${}_{-45,+26}$ & 0.320 & 16.3 & 7.8 & 0 & 13.0\% & 6308.39 & 7.74 \\
+XGBoost (T) & 1317${}_{-33,+27}$ & 0.229 & 16.6 & 10.2 & 1 & 13.6\% & 848.99 & 0.30 \\
+LightGBM (T) & 1314${}_{-28,+30}$ & 0.288 & 16.8 & 12.0 & 0 & 13.5\% & 780.12 & 0.38 \\
+ModernNCA (T) & 1306${}_{-28,+35}$ & 0.228 & 17.2 & 7.2 & 2 & 12.3\% & 6308.39 & 0.48 \\
+TabM (D) & 1287${}_{-39,+27}$ & 0.295 & 18.2 & 8.9 & 1 & 14.2\% & 13.30 & 0.15 \\
+RealMLP (T) & 1260${}_{-25,+32}$ & 0.221 & 19.3 & 13.4 & 0 & 14.1\% & 6142.88 & 0.25 \\
+EBM (T+E) & 1258${}_{-30,+34}$ & 0.186 & 19.5 & 8.6 & 0 & 16.5\% & 1323.39 & 0.21 \\
+TabDPT (D) & 1258${}_{-42,+34}$ & 0.370 & 19.6 & 5.1 & \textcolor{silver}{\textbf{4}} & 15.0\% & 29.28 & 8.88 \\
+TorchMLP (T+E) & 1255${}_{-29,+31}$ & 0.186 & 19.7 & 12.7 & 0 & 14.1\% & 3460.09 & 2.06 \\
+ModernNCA (D) & 1226${}_{-28,+32}$ & 0.157 & 21.1 & 10.3 & 1 & 17.4\% & 14.50 & 0.30 \\
+EBM (T) & 1211${}_{-22,+31}$ & 0.127 & 21.8 & 8.1 & 2 & 17.4\% & 1323.39 & 0.03 \\
+RealMLP (D) & 1204${}_{-33,+28}$ & 0.099 & 22.3 & 11.2 & 1 & 15.2\% & 35.65 & 0.30 \\
+ExtraTrees (T+E) & 1192${}_{-35,+29}$ & 0.131 & 22.8 & 14.3 & 0 & 18.6\% & 418.39 & 0.97 \\
+FastaiMLP (T+E) & 1189${}_{-28,+33}$ & 0.181 & 22.9 & 13.5 & 0 & 17.7\% & 1453.07 & 6.68 \\
+EBM (D) & 1181${}_{-32,+25}$ & 0.136 & 23.4 & 11.0 & 1 & 18.0\% & 5.90 & 0.07 \\
+ExtraTrees (T) & 1176${}_{-27,+28}$ & 0.102 & 23.7 & 12.7 & 0 & 18.9\% & 418.39 & 0.15 \\
+TorchMLP (T) & 1175${}_{-32,+27}$ & 0.097 & 23.5 & 17.9 & 0 & 15.8\% & 3460.09 & 0.12 \\
+FastaiMLP (T) & 1135${}_{-31,+26}$ & 0.089 & 25.7 & 14.9 & 0 & 19.1\% & 1453.07 & 0.64 \\
+XGBoost (D) & 1129${}_{-32,+28}$ & 0.025 & 26.0 & 22.3 & 0 & 17.7\% & 2.86 & 0.19 \\
+RandomForest (T+E) & 1127${}_{-28,+30}$ & 0.071 & 25.9 & 15.2 & 0 & 19.5\% & 563.93 & 1.00 \\
+LightGBM (D) & 1113${}_{-33,+28}$ & 0.027 & 26.7 & 23.8 & 0 & 18.2\% & 3.28 & 0.18 \\
+RandomForest (T) & 1083${}_{-31,+31}$ & 0.036 & 28.1 & 22.1 & 0 & 20.5\% & 563.93 & 0.15 \\
+TorchMLP (D) & 1012${}_{-25,+32}$ & 0.026 & 30.9 & 26.5 & 0 & 22.2\% & 9.39 & 0.15 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.004 & 31.6 & 25.3 & 0 & 25.1\% & 0.48 & 0.07 \\
+FastaiMLP (D) & 1000${}_{-34,+31}$ & 0.015 & 31.6 & 27.8 & 0 & 23.3\% & 5.20 & 0.52 \\
+ExtraTrees (D) & 992${}_{-34,+28}$ & 0.030 & 31.9 & 27.1 & 0 & 26.1\% & 0.38 & 0.07 \\
+Linear (T+E) & 912${}_{-38,+33}$ & 0.044 & 34.7 & 17.1 & 1 & 32.0\% & 97.93 & 0.19 \\
+Linear (T) & 892${}_{-30,+31}$ & 0.028 & 35.3 & 26.5 & 0 & 32.5\% & 97.93 & 0.08 \\
+Linear (D) & 859${}_{-36,+31}$ & 0.020 & 36.4 & 33.7 & 0 & 33.9\% & 2.75 & 0.09 \\
+KNN (T+E) & 727${}_{-37,+33}$ & 0.000 & 39.4 & 33.9 & 0 & 46.1\% & 3.13 & 0.16 \\
+KNN (T) & 652${}_{-46,+46}$ & 0.000 & 40.6 & 29.9 & 0 & 48.2\% & 3.13 & 0.03 \\
+KNN (D) & 360${}_{-80,+57}$ & 0.000 & 43.3 & 43.2 & 0 & 56.3\% & 0.06 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/lite/tabpfn/tabarena_leaderboard.csv b/data/lite/tabpfn/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..fd51604e2c2801e33ac016ad1e93ad724a1ad090
--- /dev/null
+++ b/data/lite/tabpfn/tabarena_leaderboard.csv
@@ -0,0 +1,45 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+TABPFNV2 (tuned + ensemble),14336.010728077455,156.01344153375337,4382.0533643641575,74.48134283766498,0.28759504252950974,0.3377998072280995,0.061011172759490306,0.042703954608678374,95784.56309893043,6432.006144478144,7.787878787878788,0.21046,7449.2451322078705,56.3117241859436,3650.5588495238276,52.332938254416526,0.16748308582785468,0.25133689839573625,0.02321606859178882,0.01681007850550054,71633.35015738624,4111.6099760549805,5.0,11,3,1,18,1531.8,30.4,27.3,0.8421423537702607,0.4477927627927628
+AutoGluon 1.3 (4h),5959.189094875798,4.367473082108931,3961.9186202053897,3.838659223481077,0.5066593479144164,0.5031130123587579,0.08889336232518867,0.053887108619865974,52768.640134241,245.21168201964974,9.651515151515152,0.22034,5402.2047798633575,2.42671799659729,2518.1361196041107,2.329453593213223,0.4597995521540763,0.4713507311669133,0.0452138062110049,0.03721823610662635,39027.82746427101,131.32895431780238,8.0,1,2,1,29,1474.6,35.2,27.9,0.7988019732205779,0.18205298316711024
+REALMLP (tuned + ensemble),17735.155029759262,4.730990727742513,6433.060002371296,5.4487082681787244,0.5227695654892056,0.5120650322000645,0.10130621837156616,0.06074937134426635,135393.06860073804,314.1828415073118,10.575757575757576,0.21073,10195.217458248138,4.599701642990112,6142.876617706428,4.516099027530876,0.48215030896347355,0.491559756921,0.058321654172537096,0.040913538766118675,111998.40741080203,285.1562934805996,9.0,1,0,4,28,1454.1,28.7,34.4,0.7773079633544749,0.17333136380772182
+TABPFNV2 (tuned),14336.010728077455,6.103090950936982,4382.0533643641575,3.0581431200087663,0.46046523778783577,0.5188209597444956,0.0877527045624097,0.06945004703125181,95784.56309893043,243.34239180847368,11.621212121212121,0.21386,7449.2451322078705,0.4960660934448242,3650.5588495238276,0.5430449730566292,0.34176855083335494,0.5086309060869947,0.0565608520318478,0.0423413312816546,71633.35015738624,33.800294797494224,8.0,1,8,3,21,1430.9,25.8,36.1,0.7529950669485553,0.238682666157629
+TABM (tuned + ensemble),7905.59104330612,1.9562460870453806,4185.709531882815,1.768466932810432,0.5158505640872573,0.5431361751910071,0.10095330849489449,0.07779881667044108,67788.46010990962,114.61474290926614,12.424242424242424,0.21175,6126.394609212875,1.3961477279663086,3407.56780996161,1.4485538005828857,0.47020083976979304,0.52429480819432,0.04867875413017375,0.04512415883221451,49987.707956643724,99.64653279572039,11.0,1,0,2,30,1404.9,35.1,25.3,0.7343199436222692,0.15429987172281395
+GBM (tuned + ensemble),1414.8606616944978,5.3146100116498545,902.1353750352665,3.8285745502016777,0.6392387450549837,0.6631109559408186,0.12435029597091696,0.08029296614256975,13042.108823335611,299.1337158047987,13.378787878787879,0.22026,1314.2295961380005,2.1158533096313477,780.1150358026446,2.128600501785187,0.6693216689945842,0.7087805886338155,0.06385145474270604,0.05353102776704557,13234.598330219944,119.30370400980756,13.5,0,0,1,32,1386.5,26.2,30.9,0.7121212121212122,0.09458883479781313
+CAT (tuned + ensemble),5998.228848081647,1.0770193735758464,3714.832915582639,1.0224930685169833,0.6581825114313259,0.6297945073423992,0.11693394683553834,0.08165622904718,53149.267800713766,63.12902616676368,14.030303030303031,0.21068,3471.3497779369354,0.9156572818756104,2234.828925740415,0.6529485519008803,0.6112565392764306,0.6242104996505731,0.05828476269775196,0.05387373518333481,22918.764355268395,48.89284806833719,13.0,0,1,1,31,1373.5,25.9,31.4,0.696969696969697,0.10785532910191802
+TABPFNV2 (default),11.6943236914548,1.0926416714986165,5.91242680443585,0.666528048525937,0.5171946106727229,0.5791551091135295,0.10381387583615634,0.08868854300049302,96.73088882379422,50.146253518643945,15.06060606060606,0.21791,9.203422546386719,0.43659281730651855,4.965901903461194,0.5452976226806641,0.3939035196311629,0.7094305649336043,0.06280305375012951,0.06015293769345357,75.26381057033151,30.54843958112344,11.0,3,4,4,22,1348.0,29.1,23.9,0.6730091613812544,0.23189064712882612
+CAT (tuned),5998.228848081647,0.1285553267507842,3714.832915582639,0.13007453601113816,0.6842728639559639,0.6885504248819497,0.12418630031972636,0.0896022810762604,53149.267800713766,8.587377880778849,15.333333333333334,0.21493,3471.3497779369354,0.06416535377502441,2234.828925740415,0.07115136708644841,0.6577077283695499,0.679930031599041,0.06876797936712631,0.05609349712267564,22918.764355268395,3.6064895871728715,14.0,0,1,1,31,1341.9,26.7,27.0,0.6666666666666666,0.09834940324674238
+TABM (tuned),7905.59104330612,0.19297228437481503,4185.709531882815,0.20786967739368611,0.6183990992477728,0.6224646452694973,0.11161000779133246,0.09116450110816081,67788.46010990962,12.857810575794538,15.333333333333334,0.21145,6126.394609212875,0.16823339462280273,3407.56780996161,0.17298539846653752,0.5745991505979262,0.6505981572663989,0.06855918585966836,0.057665303848857825,49987.707956643724,9.039602142310635,14.0,0,2,2,29,1344.6,25.8,35.3,0.6666666666666666,0.12208925959238352
+XGB (tuned + ensemble),1888.6873327096303,3.046517429929791,1158.2361646061293,3.757011355737224,0.7293222800450723,0.715820739762068,0.1340997707713229,0.09628802932833917,14952.255554140145,205.22200127699725,15.757575757575758,0.2153,1542.8871898651123,1.3964767456054688,848.994191986563,1.8656665541560775,0.7623744964555048,0.7222843291924909,0.07724634161829114,0.06320753111560178,12569.741812840497,126.36735681634848,14.0,0,0,2,31,1335.0,21.5,28.8,0.656800563777308,0.08594458301303867
+CAT (default),142.80315683104774,0.2447956186352354,124.41934267197115,0.18478647674970256,0.740681350814587,0.7436459210714602,0.134083376286267,0.09414081542188907,500.26431087714354,13.226167413169538,16.106060606060606,0.20726,12.813451290130615,0.1487717628479004,8.126988439617271,0.12587527839504942,0.8332509641822385,0.8434910311549387,0.08032670945111942,0.08404761904761912,120.27803545982526,7.6030997550901045,18.0,1,0,0,32,1327.0,33.4,26.8,0.6486962649753347,0.10326788547349249
+MNCA (tuned + ensemble),11156.128839138782,10.252970204208836,6590.840949619391,8.639408754165267,0.679738245400772,0.6394100270674522,0.13022705034800527,0.1066436921240968,103946.26001990445,584.7921673215841,16.303030303030305,0.2215,10821.423727989197,7.313557863235474,6308.3910004173395,7.737494511886756,0.7185505017354659,0.7125625863826853,0.06054389432338636,0.05812125873658276,85886.71528316107,464.6138316092033,15.5,0,2,2,29,1326.2,25.1,44.6,0.6441155743481325,0.1280991902660478
+XGB (tuned),1888.6873327096303,0.6773105679136334,1158.2361646061293,0.8819951020962133,0.7708905111580848,0.7430258064847394,0.1358951205286783,0.09911686954396351,14952.255554140145,43.913270063701376,16.606060606060606,0.21833,1542.8871898651123,0.2559163570404053,848.994191986563,0.2979882044274592,0.8207592710887125,0.7227971551467122,0.0827897084298348,0.066243759828395,12569.741812840497,16.170469837088785,15.0,1,0,0,32,1316.8,26.7,32.2,0.6370683579985905,0.09847653714118139
+GBM (tuned),1414.8606616944978,0.8018729108752627,902.1353750352665,0.7467808129875216,0.7124399645711881,0.7455920579660587,0.1350827213293653,0.09778475717045013,13042.108823335611,52.68401353539273,16.833333333333332,0.22222,1314.2295961380005,0.2889828681945801,780.1150358026446,0.3811195826851199,0.745365207761696,0.7967516354613091,0.07316130655481845,0.0701999852719947,13234.598330219944,23.12721292618487,17.0,0,1,0,32,1313.6,29.2,27.4,0.6317829457364341,0.08340160310473639
+MNCA (tuned),11156.128839138782,0.488406752095078,6590.840949619391,0.47560948312275564,0.7723596137566766,0.6796356799110586,0.12292244893259613,0.11094274217582878,103946.26001990445,29.77527390400744,17.166666666666668,0.22855,10821.423727989197,0.40321969985961914,6308.3910004173395,0.476673807114561,0.8745571721847745,0.7249265363485337,0.07201319051941868,0.06463247386035899,85886.71528316107,25.730108223021155,16.0,2,0,1,30,1306.0,34.3,27.4,0.624031007751938,0.13943961890940268
+TABM (default),30.478135679707382,0.1787743279428193,20.49279399878298,0.18760878299490344,0.7054228305321909,0.715318593489871,0.1416501573674291,0.1142675609628328,337.0087472221777,10.899849214261154,18.181818181818183,0.2188,24.11390233039856,0.13432693481445312,13.298290032000581,0.1461421244243193,0.6869868540300672,0.7790852226704958,0.08502834278092686,0.06957745058149634,137.0322141490484,7.861401175434497,18.0,1,0,1,31,1286.6,26.3,38.4,0.6004228329809725,0.11248849756004768
+REALMLP (tuned),17735.155029759262,0.22395390452760638,6433.060002371296,0.3029892240506847,0.779232820829222,0.786711402019705,0.14107038725126725,0.10377737683349993,135393.06860073804,16.18572290331018,19.333333333333332,0.22093,10195.217458248138,0.20874404907226562,6142.876617706428,0.2460546194468641,0.885134051632072,0.8907401409990414,0.09238241308793405,0.08023809523809645,111998.40741080203,13.170123583655588,17.0,0,1,0,32,1259.7,31.9,24.4,0.5736434108527132,0.07459406268073102
+EBM (tuned + ensemble),3438.1651705250597,0.30228347489328095,2025.9909898154654,0.278357915961119,0.8143931606450567,0.7605739546960194,0.16494084588356767,0.1424652018968239,28292.283296322228,19.073799904119216,19.454545454545453,0.21701,2006.7757859230042,0.22147798538208008,1323.3940540554784,0.21026261459440876,0.9460248644293228,0.9785295623374001,0.08652042406281557,0.05568300312825867,20828.647605338916,11.089686715499157,22.0,0,3,2,28,1257.9,33.4,29.5,0.5708245243128964,0.11603443862960279
+TABDPT (default),69.51610838283192,21.59643456430146,33.15048249304137,28.806748991917754,0.629987494107437,0.669848589440618,0.1504189894232722,0.10239872734567355,588.2569465373858,1654.1673313074607,19.62121212121212,0.21757,65.70441889762878,21.056044578552246,29.27648859508967,8.8838207817589,0.7203280108909103,0.8000249564975012,0.09511484956324834,0.070909641670269,498.25427608514167,1247.4102886322382,18.0,4,1,0,28,1257.9,33.6,41.7,0.5669485553206484,0.19522552078194996
+NN_TORCH (tuned + ensemble),9157.856176080126,3.1086663116108286,4282.323362629224,2.77024720786038,0.8141011522100134,0.7941988488635939,0.14096018405841734,0.10434619365969294,78848.62406573945,182.409192264201,19.727272727272727,0.21425,7053.366072177887,2.677239179611206,3460.094702877122,2.056839412206837,0.9757817342652233,0.9237194175475067,0.08543218616764037,0.0695951926212325,57294.450258986595,134.63673457491765,20.0,0,0,2,31,1254.6,30.1,28.4,0.5644820295983086,0.07865677645737007
+MNCA (default),30.355341976339165,0.40350833806124603,17.852429615073813,0.34235869991604173,0.8429271223076698,0.8024634797197773,0.1742757260670377,0.10531319376241087,270.4302626154547,22.061972158381582,21.060606060606062,0.23345,24.494569301605225,0.2764425277709961,14.495319221168756,0.3000060482138007,1.0,0.9531612138198724,0.1256505576208179,0.09743889944848891,179.96393060938846,16.251218083714438,20.0,1,1,0,31,1225.8,31.6,27.7,0.5334742776603242,0.09733484070536731
+EBM (tuned),3438.1651705250597,0.03783920316985159,2025.9909898154654,0.037231939130763475,0.8732650510599177,0.8036623595318353,0.17366806534596116,0.15766360268758092,28292.283296322228,2.285569594265516,21.803030303030305,0.22761,2006.7757859230042,0.027222394943237305,1323.3940540554784,0.025386841881354122,1.0,1.0,0.0893728877206168,0.08235501040640528,20828.647605338916,1.1835017506360512,24.0,2,0,1,30,1211.1,30.2,21.9,0.5162085976039464,0.12329523943951919
+REALMLP (default),105.41575461445433,0.23138419064608487,37.06590128185811,0.29773903605674285,0.9013016306032677,0.815323709848827,0.1523971378269817,0.10502477628843246,791.8198164679417,15.953890267817545,22.318181818181817,0.22649,57.062673807144165,0.18715763092041016,35.649430762175584,0.30133283927465565,1.0,1.0,0.10414681647940127,0.09045562976994424,625.7008793991246,13.341950411904335,24.0,1,0,0,32,1204.0,27.1,32.1,0.5042283298097252,0.08959166478898548
+XT (tuned + ensemble),708.1867504842354,1.5957839488983154,501.4565343765227,1.8224398148781864,0.8688873144004177,0.8350075171577747,0.18587020622182546,0.14066221082257024,7205.038232093607,102.24882259176883,22.818181818181817,0.22616,686.9523606300354,1.3263120651245117,418.38755533099174,0.9717198690479619,1.0,1.0,0.12222279775100575,0.0969336948409988,5299.380855419872,85.4887622756034,26.5,0,0,0,33,1192.3,29.0,34.4,0.492600422832981,0.06973746524809589
+FASTAI (tuned + ensemble),2576.8732850045867,8.659171588493116,1678.8968661467622,9.647784447253477,0.8186439717731856,0.8233840342610399,0.17744811445555947,0.12353073291472176,24684.22585148677,537.1797900399015,22.924242424242426,0.22761,2283.895311355591,7.460584878921509,1453.0691261589527,6.677908072211324,1.0,1.0,0.09544576825940065,0.08681294440231768,19729.511337209304,439.3893190512624,26.0,0,0,0,33,1189.2,32.7,27.6,0.4901338971106413,0.07388840038053952
+EBM (default),11.796755761811227,0.06572751565413042,7.163006681498326,0.08347649829212658,0.8635835253158718,0.8429534121799166,0.18042792289811066,0.15909949729002285,102.62018767841327,4.629805865519655,23.424242424242426,0.21297,8.807492733001709,0.05507326126098633,5.903346415390749,0.07107973737946784,1.0,1.0,0.10385145665987439,0.07023809523809632,94.535974439501,3.64412233970632,25.0,1,0,0,32,1180.7,25.0,31.3,0.4785059901338971,0.09056415513048684
+NN_TORCH (tuned),9157.856176080126,0.15434888637427127,4282.323362629224,0.16049520877603024,0.9028056592953255,0.8416227639000304,0.15759149268767128,0.12428490892625065,78848.62406573945,9.627911202663915,23.545454545454547,0.22879,7053.366072177887,0.11448168754577637,3460.094702877122,0.12080316789438152,1.0,1.0,0.100147151360233,0.0767812248241212,57294.450258986595,7.364003194231025,26.0,0,0,0,33,1175.1,26.1,31.1,0.47568710359408034,0.05581669262818803
+XT (tuned),708.1867504842354,0.17320121418346057,501.4565343765227,0.21536245368256726,0.8983919342324714,0.8194714168333243,0.18876898814096105,0.14265005001131126,7205.038232093607,11.521354503327439,23.727272727272727,0.22797,686.9523606300354,0.13843560218811035,418.38755533099174,0.14728453101181402,1.0,1.0,0.1376017223667837,0.08719461637839616,5299.380855419872,9.92312806131416,29.0,0,1,0,32,1176.2,27.4,26.3,0.4714587737843552,0.07889643231090135
+FASTAI (tuned),2576.8732850045867,0.7244977951049805,1678.8968661467622,0.8750374778043916,0.9106015193205547,0.8702717656688002,0.19067319655867174,0.14971818844740778,24684.22585148677,50.26766962756969,25.727272727272727,0.24751,2283.895311355591,0.6439375877380371,1453.0691261589527,0.6368904113769531,1.0,1.0,0.11504721620397895,0.09909086376803719,19729.511337209304,38.24136055200847,28.0,0,1,0,32,1135.4,25.4,30.8,0.4249471458773784,0.06710682416721182
+RF (tuned + ensemble),888.3357556805466,1.4584405927947073,548.982788660307,1.6144500937500732,0.9290486520150778,0.8921110383301076,0.19522228883414436,0.17030886938280962,7986.192963738265,94.79279363134923,25.90909090909091,0.21825,787.3376877307892,1.127267837524414,563.9330272159958,0.9968510626638947,1.0,1.0,0.139739054041219,0.10570563621989555,6864.068149879297,78.90150886133937,29.0,0,1,0,32,1127.1,29.6,27.1,0.42071881606765327,0.06564423024290174
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+NN_TORCH (default),27.938863674799602,0.20885326645591043,17.594693580825485,0.25765115579582876,0.9742281682834123,0.9498085655717742,0.2223573038490221,0.18756332476278967,276.3144916310567,14.424726839721131,30.893939393939394,0.22004,19.96923542022705,0.14620447158813477,9.388451067337588,0.15027597806179566,1.0,1.0,0.13014412363246453,0.12823638255534808,185.13016820561077,8.78781289003576,32.0,0,0,0,33,1012.4,31.2,24.7,0.3047921071176885,0.03776804310751238
+RF (default),1.2340893889918472,0.07513474695610278,0.5622938182798829,0.08939393564694124,0.9962098401734872,0.9672887065230135,0.25140766775808776,0.24715201472904136,10.185916203069231,5.20171567420914,31.575757575757574,0.23701,0.7245135307312012,0.060329437255859375,0.4841926739300568,0.0743608035126163,1.0,1.0,0.2215808246703358,0.1728884254431697,8.226093372880863,4.642861593290882,35.0,0,0,0,33,1000.0,0.0,0.0,0.28893587033121915,0.03955153794316757
+FASTAI (default),10.49087964404713,0.5738813010129061,5.992322657632511,0.6140012973845629,0.9848306008996557,0.9441644290803554,0.23284269048954082,0.1966651448852838,93.5145549982301,37.08947361427536,31.606060606060606,0.23988,7.7834436893463135,0.5681214332580566,5.198076387326242,0.522444012045375,1.0,1.0,0.1527465876997327,0.13328152321740847,81.9890544250646,35.934326641384644,34.0,0,0,0,33,999.9,30.5,33.7,0.288231148696265,0.03602082006463107
+XT (default),0.765689365791552,0.07577740062366832,0.4465789210364869,0.09028580254631946,0.9701664672021088,0.9565087495356771,0.26114772709993034,0.26011640953720233,6.382092612032232,5.230500351711613,31.87878787878788,0.23857,0.6523852348327637,0.06471061706542969,0.3792952342205737,0.07494776627979297,1.0,1.0,0.1985320515377561,0.1548442777574803,6.583290644538605,4.555164209010363,36.0,0,0,0,33,992.5,27.6,33.3,0.28188865398167723,0.036874429537043306
+LR (tuned + ensemble),170.2873171820785,0.3659532792640455,120.20623845518064,0.3201269296402589,0.9564945706206911,0.968230584030719,0.3196771170239951,0.33838034029256764,1708.0827888193253,25.164726190305664,34.72727272727273,0.24535,154.81117272377014,0.17154169082641602,97.92847529053688,0.18593118581206958,1.0,1.0,0.2711660718812774,0.2552683896620274,1268.2145563260167,9.250054132082282,39.0,1,0,0,32,911.5,32.1,37.9,0.2156448202959831,0.05844011745944829
+LR (tuned),170.2873171820785,0.1145554528091893,120.20623845518064,0.1042144714095375,0.972064260567699,0.97552603483875,0.32504329837645596,0.3478458566538538,1708.0827888193253,6.107453287725022,35.303030303030305,0.25429,154.81117272377014,0.0533757209777832,97.92847529053688,0.07892496065012959,1.0,1.0,0.2768258727778956,0.2552683896620274,1268.2145563260167,4.226983992546795,39.5,0,0,1,32,892.1,30.8,29.2,0.2022551092318534,0.03779485565304773
+LR (default),4.627540183789803,0.13403422904737067,2.892082672901587,0.14710367960415885,0.9799295757292101,0.9836682619130945,0.33924904946053636,0.39537360179646885,43.81259012975329,8.95784223571136,36.39393939393939,0.25429,4.974438667297363,0.08619952201843262,2.750714412453488,0.09439525477373414,1.0,1.0,0.2768258727778956,0.31807101339614474,41.29590265237998,5.564972891186484,40.0,0,0,0,33,858.8,30.1,35.9,0.17688513037350245,0.029640439524678194
+KNN (tuned + ensemble),14.287633382912839,0.2593685569185199,6.199230307814771,0.19812645438519322,1.0,0.9842561941589855,0.46075029134694007,0.6166126245311254,57.516079843298705,12.788063330642764,39.36363636363637,0.34601,5.862637996673584,0.10712385177612305,3.133583813905716,0.15733957985747313,1.0,1.0,0.42992491292377444,0.7074394089684692,52.1575390239796,10.510779636451476,42.0,0,0,0,33,727.3,32.4,36.7,0.10782241014799154,0.029496960390970647
+KNN (tuned),14.287633382912839,0.05143869284427527,6.199230307814771,0.03996037829183494,1.0,0.9825364908791403,0.48222725453192367,0.6731385428276928,57.516079843298705,2.4985748964581607,40.56060606060606,0.34412,5.862637996673584,0.032462120056152344,3.133583813905716,0.030069828033447266,1.0,1.0,0.5139348134152102,0.7621498558446018,52.1575390239796,1.9337322466384677,43.0,0,0,1,32,651.7,45.4,45.4,0.07998590556730091,0.033406459585363854
+KNN (default),0.25405634533275256,0.029383905006177498,0.12227935577421162,0.030852453478009863,1.0,1.0,0.5634010526029024,0.946484954284205,1.0021213058512708,1.6500080471330536,43.303030303030305,0.40785,0.12821459770202637,0.01713418960571289,0.06314403921930106,0.02475904764267444,1.0,1.0,0.5282115941198384,1.0,1.0,1.2284888285212276,44.0,0,0,0,33,360.5,56.8,79.8,0.01620859760394644,0.023134363057794087
diff --git a/data/lite/tabpfn/tuning-impact-elo-horizontal.pdf b/data/lite/tabpfn/tuning-impact-elo-horizontal.pdf
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diff --git a/data/lite/tabpfn/tuning-impact-elo-horizontal.png.zip b/data/lite/tabpfn/tuning-impact-elo-horizontal.png.zip
new file mode 100644
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+version https://git-lfs.github.com/spec/v1
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+size 135098
diff --git a/data/tabicl-cls/figures/critical-diagram.pdf b/data/tabicl-cls/figures/critical-diagram.pdf
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+version https://git-lfs.github.com/spec/v1
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diff --git a/data/tabicl-cls/leaderboard.tex b/data/tabicl-cls/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..f6a356299f52996338ff47cc4a594c8f45ef23e4
--- /dev/null
+++ b/data/tabicl-cls/leaderboard.tex
@@ -0,0 +1,50 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+AutoGluon 1.3 (4h) & \textcolor{gold}{\textbf{1645${}_{-36,+32}$}} & \textcolor{gold}{\textbf{0.569}} & \textcolor{gold}{\textbf{7.9}} & \textcolor{silver}{\textbf{2.9}} & \textcolor{silver}{\textbf{7}} & \textcolor{gold}{\textbf{5.4\%}} & 1222.55 & 2.36 \\
+TabM (T+E) & \textcolor{silver}{\textbf{1634${}_{-32,+36}$}} & \textcolor{bronze}{\textbf{0.491}} & \textcolor{silver}{\textbf{8.3}} & \textcolor{bronze}{\textbf{3.4}} & \textcolor{bronze}{\textbf{4}} & \textcolor{bronze}{\textbf{6.7\%}} & 2387.83 & 1.47 \\
+TabICL (D) & \textcolor{bronze}{\textbf{1599${}_{-40,+31}$}} & \textcolor{silver}{\textbf{0.521}} & \textcolor{bronze}{\textbf{9.4}} & \textcolor{gold}{\textbf{2.8}} & \textcolor{gold}{\textbf{8}} & \textcolor{gold}{\textbf{5.4\%}} & 8.68 & 1.81 \\
+RealMLP (T+E) & 1598${}_{-34,+29}$ & 0.418 & 9.5 & 5.5 & 1 & 8.1\% & 5472.30 & 3.06 \\
+LightGBM (T+E) & 1584${}_{-23,+33}$ & 0.389 & 9.9 & 5.7 & 1 & 8.8\% & 374.34 & 1.46 \\
+TabM (T) & 1537${}_{-32,+35}$ & 0.409 & 11.6 & 5.4 & 1 & 7.8\% & 2387.83 & 0.16 \\
+CatBoost (T+E) & 1530${}_{-29,+28}$ & 0.377 & 11.9 & 7.0 & 1 & 7.7\% & 1233.49 & 0.52 \\
+CatBoost (T) & 1522${}_{-27,+30}$ & 0.360 & 12.3 & 6.3 & 1 & 7.9\% & 1233.49 & 0.07 \\
+LightGBM (T) & 1504${}_{-26,+33}$ & 0.302 & 13.0 & 10.9 & 0 & 9.5\% & 374.34 & 0.22 \\
+CatBoost (D) & 1502${}_{-25,+23}$ & 0.343 & 13.0 & 6.4 & 1 & 9.2\% & 5.31 & 0.07 \\
+XGBoost (T+E) & 1497${}_{-31,+30}$ & 0.320 & 13.2 & 9.3 & 0 & 9.6\% & 637.94 & 1.22 \\
+ModernNCA (T) & 1462${}_{-31,+33}$ & 0.275 & 14.7 & 7.1 & 2 & 9.2\% & 4614.64 & 0.52 \\
+ModernNCA (T+E) & 1455${}_{-33,+30}$ & 0.383 & 15.0 & 6.0 & 1 & 9.1\% & 4614.64 & 8.40 \\
+XGBoost (T) & 1450${}_{-30,+33}$ & 0.264 & 15.0 & 12.4 & 0 & 10.0\% & 637.94 & 0.17 \\
+TabM (D) & 1425${}_{-27,+31}$ & 0.272 & 16.3 & 10.0 & 0 & 11.5\% & 9.82 & 0.13 \\
+TorchMLP (T+E) & 1401${}_{-23,+28}$ & 0.228 & 17.1 & 12.2 & 0 & 10.5\% & 2372.22 & 2.09 \\
+RealMLP (T) & 1399${}_{-38,+31}$ & 0.198 & 17.4 & 12.6 & 0 & 11.3\% & 5472.30 & 0.17 \\
+EBM (T+E) & 1391${}_{-27,+26}$ & 0.170 & 17.7 & 10.7 & 0 & 13.9\% & 895.61 & 0.20 \\
+FastaiMLP (T+E) & 1367${}_{-30,+31}$ & 0.207 & 18.8 & 10.6 & 0 & 13.9\% & 582.77 & 4.52 \\
+ModernNCA (D) & 1355${}_{-26,+29}$ & 0.142 & 19.3 & 10.4 & 1 & 13.1\% & 14.53 & 0.34 \\
+EBM (T) & 1328${}_{-36,+26}$ & 0.110 & 20.6 & 15.1 & 0 & 14.7\% & 895.61 & 0.02 \\
+EBM (D) & 1292${}_{-29,+29}$ & 0.119 & 22.1 & 7.9 & 3 & 15.6\% & 3.72 & 0.04 \\
+XGBoost (D) & 1277${}_{-22,+34}$ & 0.102 & 22.6 & 18.0 & 0 & 13.2\% & 1.58 & 0.11 \\
+RealMLP (D) & 1274${}_{-30,+27}$ & 0.095 & 22.9 & 18.8 & 0 & 13.3\% & 35.20 & 0.18 \\
+TabDPT (D) & 1269${}_{-30,+35}$ & 0.186 & 23.0 & 6.2 & 3 & 14.7\% & 20.51 & 8.54 \\
+TorchMLP (T) & 1269${}_{-22,+32}$ & 0.085 & 23.0 & 19.1 & 0 & 13.2\% & 2372.22 & 0.15 \\
+ExtraTrees (T+E) & 1263${}_{-28,+29}$ & 0.082 & 23.3 & 15.9 & 0 & 15.2\% & 182.30 & 0.74 \\
+FastaiMLP (T) & 1259${}_{-29,+29}$ & 0.094 & 23.5 & 18.2 & 0 & 16.0\% & 582.77 & 0.29 \\
+RandomForest (T+E) & 1210${}_{-24,+31}$ & 0.067 & 25.5 & 15.4 & 0 & 16.0\% & 260.01 & 0.74 \\
+LightGBM (D) & 1206${}_{-25,+30}$ & 0.053 & 25.7 & 23.9 & 0 & 14.8\% & 1.41 & 0.12 \\
+ExtraTrees (T) & 1205${}_{-27,+30}$ & 0.056 & 25.8 & 20.7 & 0 & 16.7\% & 182.30 & 0.07 \\
+RandomForest (T) & 1156${}_{-28,+29}$ & 0.045 & 27.7 & 21.1 & 0 & 17.3\% & 260.01 & 0.07 \\
+TorchMLP (D) & 1103${}_{-32,+34}$ & 0.022 & 29.6 & 26.8 & 0 & 18.6\% & 6.03 & 0.13 \\
+FastaiMLP (D) & 1073${}_{-31,+34}$ & 0.027 & 30.6 & 27.5 & 0 & 21.5\% & 2.81 & 0.32 \\
+Linear (T+E) & 1030${}_{-39,+33}$ & 0.044 & 32.0 & 23.1 & 0 & 26.9\% & 44.46 & 0.20 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.003 & 32.8 & 31.3 & 0 & 23.4\% & 0.33 & 0.04 \\
+Linear (T) & 989${}_{-24,+35}$ & 0.028 & 33.1 & 26.3 & 0 & 27.9\% & 44.46 & 0.07 \\
+Linear (D) & 979${}_{-29,+35}$ & 0.020 & 33.4 & 17.4 & 1 & 29.2\% & 1.43 & 0.09 \\
+ExtraTrees (D) & 929${}_{-38,+35}$ & 0.011 & 34.7 & 31.5 & 0 & 26.1\% & 0.24 & 0.04 \\
+KNN (T+E) & 694${}_{-47,+39}$ & 0.000 & 38.8 & 38.5 & 0 & 49.2\% & 3.44 & 0.18 \\
+KNN (T) & 597${}_{-48,+54}$ & 0.000 & 39.8 & 39.7 & 0 & 51.1\% & 3.44 & 0.04 \\
+KNN (D) & 407${}_{-77,+83}$ & 0.000 & 41.2 & 41.1 & 0 & 59.7\% & 0.07 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/tabicl-cls/tabarena_leaderboard.csv b/data/tabicl-cls/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9ff26eed457798f850c0be6a353f123ed8bd3afa
--- /dev/null
+++ b/data/tabicl-cls/tabarena_leaderboard.csv
@@ -0,0 +1,43 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+AutoGluon 1.3 (4h),7750.377155031171,22.84719785201697,2751.5674214709147,2.9633558383567813,0.4305752039617605,0.3953654066045534,0.05350042752511665,0.03292137769247373,32689.64667720609,276.28827431069953,7.916666666666667,0.159155,6752.988276031282,3.4718479580349393,1222.5529738029095,2.35500290690449,0.3593825434326649,0.3493492611774055,0.022091856756724426,0.014502276761886618,26140.06177622487,154.15434639040092,5.0,7,3,2,24,1645.2,31.5,36.0,0.8313008130081301,0.34643609213236404
+TABM (tuned + ensemble),32820.849396629244,7.890166007074309,3102.495096524168,1.8344804606248617,0.5092283925039975,0.4916904001038966,0.06741513792808897,0.0531492276149925,48157.60289475875,128.99098784852723,8.277777777777779,0.17235499999999998,8134.35737352901,2.5847251441743637,2387.831479177676,1.4722284599091153,0.47136645123500964,0.47309371248601884,0.032755260863256086,0.01377708489439205,44088.87353980956,121.43882105497326,5.0,4,6,3,23,1634.2,35.1,31.2,0.8224932249322493,0.29539017627252917
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+FASTAI (default),31.09969735491423,1.10650484436824,4.683882685740998,0.49602131438348224,0.972512597016023,0.937491255300511,0.214763192071676,0.16309292278867282,78.50334203533181,29.20329942994179,30.61111111111111,0.19183499999999998,12.376497785250347,0.7602158255047269,2.8056596594025227,0.32364197153238683,1.0,0.9999615975422427,0.16139709053294848,0.10101015982783891,62.830982755166104,26.31473180904403,33.5,0,0,0,36,1072.9,33.5,31.0,0.2777777777777778,0.03642319913980632
+LR (tuned + ensemble),247.81690773551847,1.5417047552120537,86.9527487524539,0.32565721661614494,0.9561054304859822,0.945372194390879,0.26906892579103764,0.2196796767290125,1145.6529776499187,22.80693330823688,32.02777777777778,0.20382,171.4822693798277,0.2983522944980197,44.45837271402539,0.19711479228248058,1.0,1.0,0.2029910383523567,0.12545333458842928,713.1690352739483,13.25530093456624,35.0,0,0,1,35,1029.6,32.8,38.1,0.24322493224932248,0.04334753037904041
+RF (default),3.0601362299771955,0.1389405298380204,0.4197356009776281,0.0653398591441877,0.9965256868256966,0.9750534412705346,0.23374289847938945,0.22890241001401435,6.380264980433452,3.8863601848700204,32.84722222222222,0.21025,1.1198331514994302,0.08397722244262695,0.3277067685609757,0.03542500892500126,1.0,1.0,0.17586669886220047,0.11692775501298655,5.654732996519019,3.4893450212553976,34.75,0,0,0,36,1000.0,0.0,0.0,0.22323848238482386,0.03196753393423037
+LR (tuned),247.81690773551847,0.4449969635333544,86.9527487524539,0.10396867681281166,0.9723830890370608,0.9526957165635138,0.2793124361704181,0.22914434571494902,1145.6529776499187,6.67768937474542,33.111111111111114,0.20357,171.4822693798277,0.11908173561096191,44.45837271402539,0.07001089403664298,1.0,1.0,0.209915797201436,0.13903865882301508,713.1690352739483,4.422167155622043,36.0,0,0,0,36,989.1,34.1,23.6,0.21680216802168023,0.03806036380315983
+LR (default),6.169420995020572,0.45878614010634244,2.106396664251635,0.12050053609932035,0.9795929927195421,0.9593614170235713,0.2916458967947873,0.2548930671246872,29.155480122785303,7.705503358920759,33.388888888888886,0.20875,5.325402127371894,0.12779696782430014,1.4313534079421033,0.0896510462814927,1.0,1.0,0.2098231228117865,0.1419072705068749,21.98095065107635,4.735441569338942,36.5,1,0,0,35,979.0,34.7,28.5,0.21002710027100271,0.05743750876797652
+XT (default),1.8043978815461381,0.17975940917745048,0.3713595805715374,0.06954273446410252,0.9892721532660226,0.9709853179280655,0.2609714953848282,0.2584310419389792,5.400149437442951,4.422216908920569,34.69444444444444,0.21292,0.9855960739983453,0.08559976683722602,0.24198385110028492,0.03905757784211604,1.0,1.0,0.18424442460299273,0.13284238574591212,4.712255061768779,3.9447928104085292,36.0,0,0,0,36,928.7,34.2,37.2,0.17818428184281843,0.031705419003530376
+KNN (tuned + ensemble),167.8303013191547,12.105770299979199,8.709396553002023,0.6510287601474662,1.0,0.9950381724620512,0.4922467034154276,0.6086097785091938,80.7590848163391,80.9066319119832,38.84722222222222,0.31954499999999997,14.630206929312813,0.18374058273103502,3.4367890290794296,0.18360265549810623,1.0,1.0,0.4552264030449861,0.6664113509907352,59.32150817198775,12.487074071232104,40.0,0,0,0,36,694.5,38.1,46.7,0.0768970189701897,0.025970231106033758
+KNN (tuned),167.8303013191547,1.8655599888460135,8.709396553002023,0.10631861792797924,1.0,0.9973606072760356,0.5109632324100885,0.654584490441368,80.7590848163391,12.961828932017745,39.84722222222222,0.32433,14.630206929312813,0.07810062832302517,3.4367890290794296,0.039402452723266784,1.0,1.0,0.4955223463378593,0.7068928996407389,59.32150817198775,2.342964973116946,41.0,0,0,0,36,596.8,53.8,47.6,0.05250677506775068,0.02518666084837813
+KNN (default),0.8106582238350385,0.22655490967962477,0.11174909219233083,0.031188125017345747,1.0,1.0,0.5965726261878597,0.9589083406549634,1.0058498096362567,2.4347704716843976,41.25,0.392575,0.22842825783623588,0.035103811158074275,0.06799989431884934,0.01944144969012079,1.0,1.0,0.6183072807462191,1.0,1.0,1.2342542936555674,42.0,0,0,0,36,406.8,82.2,76.6,0.018292682926829267,0.02432448428372201
diff --git a/data/tabicl-cls/tuning-impact-elo-horizontal.pdf b/data/tabicl-cls/tuning-impact-elo-horizontal.pdf
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diff --git a/data/tabicl-imputed-cls/figures/critical-diagram.pdf b/data/tabicl-imputed-cls/figures/critical-diagram.pdf
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diff --git a/data/tabicl-imputed-cls/leaderboard.tex b/data/tabicl-imputed-cls/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..c1cecfa9c3986b7c8e4d9a1d998ac552be5d950a
--- /dev/null
+++ b/data/tabicl-imputed-cls/leaderboard.tex
@@ -0,0 +1,53 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+AutoGluon 1.3 (4h) & \textcolor{gold}{\textbf{1599${}_{-29,+34}$}} & \textcolor{gold}{\textbf{0.569}} & \textcolor{gold}{\textbf{9.4}} & \textcolor{bronze}{\textbf{3.5}} & \textcolor{bronze}{\textbf{5}} & \textcolor{gold}{\textbf{7.0\%}} & 1222.55 & 2.36 \\
+TabM (T+E) & \textcolor{silver}{\textbf{1588${}_{-30,+41}$}} & 0.491 & \textcolor{silver}{\textbf{9.7}} & 4.4 & 3 & \textcolor{bronze}{\textbf{8.6\%}} & 2387.83 & 1.47 \\
+TabICL (D) & \textcolor{bronze}{\textbf{1565${}_{-31,+33}$}} & \textcolor{silver}{\textbf{0.521}} & \textcolor{bronze}{\textbf{10.7}} & \textcolor{silver}{\textbf{3.4}} & \textcolor{silver}{\textbf{6}} & \textcolor{silver}{\textbf{7.3\%}} & 8.68 & 1.81 \\
+RealMLP (T+E) & 1555${}_{-38,+32}$ & 0.418 & 11.1 & 7.5 & 0 & 9.6\% & 5472.30 & 3.06 \\
+LightGBM (T+E) & 1547${}_{-31,+28}$ & 0.389 & 11.4 & 6.4 & 1 & 10.3\% & 374.34 & 1.46 \\
+TabM (T) & 1506${}_{-32,+31}$ & 0.409 & 13.1 & 6.2 & 1 & 9.6\% & 2387.83 & 0.16 \\
+CatBoost (T+E) & 1495${}_{-28,+28}$ & 0.377 & 13.5 & 8.7 & 0 & 9.5\% & 1233.49 & 0.52 \\
+CatBoost (T) & 1487${}_{-32,+35}$ & 0.360 & 13.9 & 6.9 & 1 & 9.7\% & 1233.49 & 0.07 \\
+TabPFNv2 (T+E) & 1484${}_{-35,+31}$ & \textcolor{bronze}{\textbf{0.506}} & 14.0 & \textcolor{gold}{\textbf{2.9}} & \textcolor{gold}{\textbf{8}} & 9.2\% & 3031.01 & 27.04 \\
+LightGBM (T) & 1472${}_{-37,+29}$ & 0.302 & 14.6 & 12.3 & 0 & 11.0\% & 374.34 & 0.22 \\
+CatBoost (D) & 1471${}_{-31,+28}$ & 0.343 & 14.6 & 6.9 & 1 & 10.7\% & 5.31 & 0.07 \\
+XGBoost (T+E) & 1466${}_{-28,+29}$ & 0.320 & 14.8 & 10.1 & 0 & 11.1\% & 637.94 & 1.22 \\
+ModernNCA (T) & 1434${}_{-33,+35}$ & 0.275 & 16.3 & 9.5 & 1 & 10.8\% & 4614.64 & 0.52 \\
+ModernNCA (T+E) & 1428${}_{-33,+23}$ & 0.383 & 16.6 & 7.3 & 0 & 10.8\% & 4614.64 & 8.40 \\
+XGBoost (T) & 1426${}_{-35,+32}$ & 0.264 & 16.8 & 13.7 & 0 & 11.6\% & 637.94 & 0.17 \\
+TabPFNv2 (T) & 1411${}_{-34,+32}$ & 0.393 & 17.3 & 4.9 & 1 & 11.7\% & 3031.01 & 0.59 \\
+TabM (D) & 1400${}_{-27,+26}$ & 0.272 & 18.0 & 11.8 & 0 & 12.9\% & 9.82 & 0.13 \\
+TabPFNv2 (D) & 1391${}_{-38,+28}$ & 0.360 & 18.5 & 4.5 & 4 & 12.7\% & 3.33 & 0.33 \\
+TorchMLP (T+E) & 1381${}_{-30,+28}$ & 0.228 & 18.9 & 14.6 & 0 & 11.9\% & 2372.22 & 2.09 \\
+RealMLP (T) & 1374${}_{-26,+34}$ & 0.198 & 19.2 & 15.0 & 0 & 12.7\% & 5472.30 & 0.17 \\
+EBM (T+E) & 1371${}_{-35,+26}$ & 0.170 & 19.5 & 13.5 & 0 & 15.5\% & 895.61 & 0.20 \\
+FastaiMLP (T+E) & 1347${}_{-32,+32}$ & 0.207 & 20.6 & 12.3 & 0 & 14.9\% & 582.77 & 4.52 \\
+ModernNCA (D) & 1338${}_{-37,+29}$ & 0.142 & 21.1 & 11.9 & 1 & 14.5\% & 14.53 & 0.34 \\
+EBM (T) & 1309${}_{-29,+28}$ & 0.110 & 22.5 & 17.9 & 0 & 16.2\% & 895.61 & 0.02 \\
+EBM (D) & 1274${}_{-27,+35}$ & 0.119 & 24.1 & 11.6 & 1 & 17.0\% & 3.72 & 0.04 \\
+XGBoost (D) & 1266${}_{-30,+28}$ & 0.102 & 24.6 & 19.3 & 0 & 14.6\% & 1.58 & 0.11 \\
+RealMLP (D) & 1262${}_{-24,+34}$ & 0.095 & 24.8 & 20.6 & 0 & 14.7\% & 35.20 & 0.18 \\
+TorchMLP (T) & 1259${}_{-38,+31}$ & 0.085 & 25.0 & 21.6 & 0 & 14.5\% & 2372.22 & 0.15 \\
+TabDPT (D) & 1258${}_{-32,+34}$ & 0.186 & 25.0 & 7.9 & 2 & 15.9\% & 20.51 & 8.54 \\
+ExtraTrees (T+E) & 1254${}_{-30,+32}$ & 0.082 & 25.2 & 18.9 & 0 & 16.5\% & 182.30 & 0.74 \\
+FastaiMLP (T) & 1244${}_{-26,+38}$ & 0.094 & 25.6 & 20.6 & 0 & 16.9\% & 582.77 & 0.29 \\
+RandomForest (T+E) & 1206${}_{-33,+26}$ & 0.067 & 27.5 & 17.4 & 0 & 17.5\% & 260.01 & 0.74 \\
+LightGBM (D) & 1200${}_{-31,+31}$ & 0.053 & 27.7 & 25.8 & 0 & 16.1\% & 1.41 & 0.12 \\
+ExtraTrees (T) & 1196${}_{-36,+29}$ & 0.056 & 27.8 & 23.8 & 0 & 17.9\% & 182.30 & 0.07 \\
+RandomForest (T) & 1153${}_{-34,+27}$ & 0.045 & 29.8 & 22.7 & 0 & 18.7\% & 260.01 & 0.07 \\
+TorchMLP (D) & 1101${}_{-33,+36}$ & 0.022 & 31.8 & 29.0 & 0 & 19.7\% & 6.03 & 0.13 \\
+FastaiMLP (D) & 1070${}_{-35,+35}$ & 0.027 & 32.9 & 29.7 & 0 & 22.3\% & 2.81 & 0.32 \\
+Linear (T+E) & 1029${}_{-33,+28}$ & 0.044 & 34.4 & 24.9 & 0 & 28.0\% & 44.46 & 0.20 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.003 & 35.3 & 33.7 & 0 & 24.4\% & 0.33 & 0.04 \\
+Linear (T) & 991${}_{-29,+34}$ & 0.028 & 35.6 & 30.0 & 0 & 28.9\% & 44.46 & 0.07 \\
+Linear (D) & 984${}_{-37,+30}$ & 0.020 & 35.9 & 27.1 & 0 & 30.0\% & 1.43 & 0.09 \\
+ExtraTrees (D) & 923${}_{-38,+30}$ & 0.011 & 37.5 & 34.5 & 0 & 26.9\% & 0.24 & 0.04 \\
+KNN (T+E) & 686${}_{-48,+31}$ & 0.000 & 41.8 & 41.5 & 0 & 49.7\% & 3.44 & 0.18 \\
+KNN (T) & 597${}_{-47,+39}$ & 0.000 & 42.8 & 42.7 & 0 & 51.5\% & 3.44 & 0.04 \\
+KNN (D) & 410${}_{-78,+56}$ & 0.000 & 44.2 & 44.1 & 0 & 60.0\% & 0.07 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/tabicl-imputed-cls/tabarena_leaderboard.csv b/data/tabicl-imputed-cls/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e24e41bd4a3334eedf84f09b871d0111db4e9ea3
--- /dev/null
+++ b/data/tabicl-imputed-cls/tabarena_leaderboard.csv
@@ -0,0 +1,46 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
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+TABM (tuned + ensemble),32820.849396629244,7.890166007074309,3102.495096524168,1.8344804606248617,0.5092283925039975,0.5350797555443023,0.08573855686320273,0.05963111030471803,48157.60289475875,128.99098784852723,9.694444444444445,0.17235499999999998,8134.35737352901,2.5847251441743637,2387.831479177676,1.4722284599091153,0.47136645123500964,0.5540981099375837,0.03899671266638771,0.02990990162448854,44088.87353980956,121.43882105497326,8.0,3,2,2,29,1588.1,40.9,29.4,0.80239898989899,0.22816337166827363
+TABICL (default),112.87139065964723,20.33866377333064,9.733153451045638,2.345033367232273,0.4788051732855598,0.5342388690836984,0.0733517897000551,0.04928098549249302,178.07934402242574,242.8201765656135,10.652777777777779,0.17207,25.732762111557854,3.564396858215332,8.684246340890724,1.808164283651731,0.4992564187967412,0.567554590317891,0.03487584587193143,0.01673206206271185,151.54793227614402,132.97465082181864,9.5,6,4,1,25,1565.3,32.1,30.1,0.7806186868686869,0.2984959355396537
+REALMLP (tuned + ensemble),78320.7099595922,13.457468075516783,5777.34529914899,4.747200702818298,0.5822217846671746,0.5763475950282718,0.09627181319390925,0.054761231537571176,141412.64388451062,278.6382916983155,11.055555555555555,0.166985,23101.338454975023,5.6439330816268924,5472.29624726055,3.0648418488665286,0.5258237901453162,0.5623887498677591,0.051850568481074444,0.029565742001882556,99490.3161960261,233.98656302124476,9.0,0,0,3,33,1554.6,31.9,37.4,0.7714646464646465,0.13261942492574919
+GBM (tuned + ensemble),2757.3124551578803,12.456019589415302,657.304398009506,2.529125543711048,0.6113071815088934,0.6163296836138819,0.10258436279694044,0.06118717781173281,9019.445489261983,194.08976472235491,11.402777777777779,0.16698000000000002,1484.5727322684393,3.4757837878333198,374.34433555986243,1.4573305804667545,0.6534872634143774,0.6349889770045265,0.051567085599247775,0.02199552512560315,8683.556526223521,104.28918180708692,11.0,1,1,3,31,1546.6,27.1,30.9,0.7635732323232324,0.15604065347685897
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+CAT (tuned + ensemble),14620.521881076362,2.36174942917294,2269.8595810438947,0.6522846741920234,0.6226459246322733,0.6119512748454302,0.09491452657978199,0.05316377757060419,28484.88552620336,44.38703157771985,13.541666666666666,0.16040500000000002,4812.184866507848,1.2190414004855685,1233.4944988708792,0.5157389806383175,0.6153632608570143,0.669054345099479,0.05899249898577069,0.023358363194257874,20694.132105891145,37.76547447057217,12.5,0,1,2,33,1495.4,27.3,27.9,0.7149621212121212,0.11479127151934615
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+FASTAI (default),31.09969735491423,1.10650484436824,4.683882685740998,0.49602131438348224,0.972512597016023,0.93827750568811,0.22259183987469472,0.1676085097457016,78.50334203533181,29.20329942994179,32.94444444444444,0.19183499999999998,12.376497785250347,0.7602158255047269,2.8056596594025227,0.32364197153238683,1.0,0.9998853211009175,0.16139709053294848,0.10101015982783891,62.830982755166104,26.31473180904403,36.0,0,0,0,36,1069.9,34.4,34.9,0.273989898989899,0.03368733318328171
+LR (tuned + ensemble),247.81690773551847,1.5417047552120537,86.9527487524539,0.32565721661614494,0.9561054304859822,0.9503142970931224,0.2795482007705742,0.22419398362837153,1145.6529776499187,22.80693330823688,34.416666666666664,0.20382,171.4822693798277,0.2983522944980197,44.45837271402539,0.19711479228248058,1.0,1.0,0.2029910383523567,0.12993275981723412,713.1690352739483,13.25530093456624,37.5,0,0,1,35,1029.4,27.5,32.1,0.24053030303030304,0.0401701082123688
+RF (default),3.0601362299771955,0.1389405298380204,0.4197356009776281,0.0653398591441877,0.9965256868256966,0.9784714305124642,0.24439814041789643,0.23332089200953096,6.380264980433452,3.8863601848700204,35.263888888888886,0.21025,1.1198331514994302,0.08397722244262695,0.3277067685609757,0.03542500892500126,1.0,1.0,0.17586669886220047,0.11692775501298655,5.654732996519019,3.4893450212553976,37.25,0,0,0,36,1000.0,0.0,0.0,0.2212752525252525,0.02965019969409202
+LR (tuned),247.81690773551847,0.4449969635333544,86.9527487524539,0.10396867681281166,0.9723830890370608,0.957603281359881,0.2890832368031623,0.2335985252567378,1145.6529776499187,6.67768937474542,35.55555555555556,0.20357,171.4822693798277,0.11908173561096191,44.45837271402539,0.07001089403664298,1.0,1.0,0.209915797201436,0.13996577713587863,713.1690352739483,4.422167155622043,38.0,0,0,0,36,990.7,33.7,28.9,0.21464646464646464,0.03335309519895627
+LR (default),6.169420995020572,0.45878614010634244,2.106396664251635,0.12050053609932035,0.9795929927195421,0.9635148500480292,0.2995361357732522,0.25915777266480783,29.155480122785303,7.705503358920759,35.861111111111114,0.20875,5.325402127371894,0.12779696782430014,1.4313534079421033,0.0896510462814927,1.0,1.0,0.2098231228117865,0.14283438851684813,21.98095065107635,4.735441569338942,39.5,0,0,1,35,984.0,29.4,36.7,0.2077020202020202,0.036834411850132504
+XT (default),1.8043978815461381,0.17975940917745048,0.3713595805715374,0.06954273446410252,0.9892721532660226,0.9741636720839656,0.2693928274585231,0.26259240866880457,5.400149437442951,4.422216908920569,37.47222222222222,0.21292,0.9855960739983453,0.08559976683722602,0.24198385110028492,0.03905757784211604,1.0,1.0,0.18424442460299273,0.1366333464189622,4.712255061768779,3.9447928104085292,39.0,0,0,0,36,922.9,29.7,37.5,0.1710858585858586,0.028997498234233365
+KNN (tuned + ensemble),167.8303013191547,12.105770299979199,8.709396553002023,0.6510287601474662,1.0,0.9960654060232148,0.49677010357074014,0.6120942109628191,80.7590848163391,80.9066319119832,41.80555555555556,0.31954499999999997,14.630206929312813,0.18374058273103502,3.4367890290794296,0.18360265549810623,1.0,1.0,0.4552264030449861,0.6664113509907352,59.32150817198775,12.487074071232104,43.0,0,0,0,36,685.5,30.3,47.5,0.0726010101010101,0.024106030242002884
+KNN (tuned),167.8303013191547,1.8655599888460135,8.709396553002023,0.10631861792797924,1.0,0.9975847139031918,0.5153228769885266,0.6579120926382002,80.7590848163391,12.961828932017745,42.80555555555556,0.32433,14.630206929312813,0.07810062832302517,3.4367890290794296,0.039402452723266784,1.0,1.0,0.4955223463378593,0.7068928996407389,59.32150817198775,2.342964973116946,44.0,0,0,0,36,596.6,38.1,46.2,0.049873737373737376,0.02344196684773788
+KNN (default),0.8106582238350385,0.22655490967962477,0.11174909219233083,0.031188125017345747,1.0,1.0,0.5999865293650463,0.9596846471567678,1.0058498096362567,2.4347704716843976,44.208333333333336,0.392575,0.22842825783623588,0.035103811158074275,0.06799989431884934,0.01944144969012079,1.0,1.0,0.619455920315494,1.0,1.0,1.2342542936555674,45.0,0,0,0,36,409.8,56.0,77.2,0.017992424242424244,0.0226951547710286
diff --git a/data/tabicl-imputed-cls/tuning-impact-elo-horizontal.pdf b/data/tabicl-imputed-cls/tuning-impact-elo-horizontal.pdf
new file mode 100644
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diff --git a/data/tabicl-imputed-cls/tuning-impact-elo-horizontal.png.zip b/data/tabicl-imputed-cls/tuning-impact-elo-horizontal.png.zip
new file mode 100644
index 0000000000000000000000000000000000000000..239598955be83ce2de1491934bc83495b5eb0560
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+version https://git-lfs.github.com/spec/v1
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+size 152921
diff --git a/data/tabicl-imputed/figures/critical-diagram.pdf b/data/tabicl-imputed/figures/critical-diagram.pdf
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diff --git a/data/tabicl-imputed/figures/critical-diagram.png.zip b/data/tabicl-imputed/figures/critical-diagram.png.zip
new file mode 100644
index 0000000000000000000000000000000000000000..dbc8af23f2ed54c53adf16bb2f35a7df366e96ed
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+version https://git-lfs.github.com/spec/v1
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diff --git a/data/tabicl-imputed/leaderboard.tex b/data/tabicl-imputed/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..c1cecfa9c3986b7c8e4d9a1d998ac552be5d950a
--- /dev/null
+++ b/data/tabicl-imputed/leaderboard.tex
@@ -0,0 +1,53 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+AutoGluon 1.3 (4h) & \textcolor{gold}{\textbf{1599${}_{-29,+34}$}} & \textcolor{gold}{\textbf{0.569}} & \textcolor{gold}{\textbf{9.4}} & \textcolor{bronze}{\textbf{3.5}} & \textcolor{bronze}{\textbf{5}} & \textcolor{gold}{\textbf{7.0\%}} & 1222.55 & 2.36 \\
+TabM (T+E) & \textcolor{silver}{\textbf{1588${}_{-30,+41}$}} & 0.491 & \textcolor{silver}{\textbf{9.7}} & 4.4 & 3 & \textcolor{bronze}{\textbf{8.6\%}} & 2387.83 & 1.47 \\
+TabICL (D) & \textcolor{bronze}{\textbf{1565${}_{-31,+33}$}} & \textcolor{silver}{\textbf{0.521}} & \textcolor{bronze}{\textbf{10.7}} & \textcolor{silver}{\textbf{3.4}} & \textcolor{silver}{\textbf{6}} & \textcolor{silver}{\textbf{7.3\%}} & 8.68 & 1.81 \\
+RealMLP (T+E) & 1555${}_{-38,+32}$ & 0.418 & 11.1 & 7.5 & 0 & 9.6\% & 5472.30 & 3.06 \\
+LightGBM (T+E) & 1547${}_{-31,+28}$ & 0.389 & 11.4 & 6.4 & 1 & 10.3\% & 374.34 & 1.46 \\
+TabM (T) & 1506${}_{-32,+31}$ & 0.409 & 13.1 & 6.2 & 1 & 9.6\% & 2387.83 & 0.16 \\
+CatBoost (T+E) & 1495${}_{-28,+28}$ & 0.377 & 13.5 & 8.7 & 0 & 9.5\% & 1233.49 & 0.52 \\
+CatBoost (T) & 1487${}_{-32,+35}$ & 0.360 & 13.9 & 6.9 & 1 & 9.7\% & 1233.49 & 0.07 \\
+TabPFNv2 (T+E) & 1484${}_{-35,+31}$ & \textcolor{bronze}{\textbf{0.506}} & 14.0 & \textcolor{gold}{\textbf{2.9}} & \textcolor{gold}{\textbf{8}} & 9.2\% & 3031.01 & 27.04 \\
+LightGBM (T) & 1472${}_{-37,+29}$ & 0.302 & 14.6 & 12.3 & 0 & 11.0\% & 374.34 & 0.22 \\
+CatBoost (D) & 1471${}_{-31,+28}$ & 0.343 & 14.6 & 6.9 & 1 & 10.7\% & 5.31 & 0.07 \\
+XGBoost (T+E) & 1466${}_{-28,+29}$ & 0.320 & 14.8 & 10.1 & 0 & 11.1\% & 637.94 & 1.22 \\
+ModernNCA (T) & 1434${}_{-33,+35}$ & 0.275 & 16.3 & 9.5 & 1 & 10.8\% & 4614.64 & 0.52 \\
+ModernNCA (T+E) & 1428${}_{-33,+23}$ & 0.383 & 16.6 & 7.3 & 0 & 10.8\% & 4614.64 & 8.40 \\
+XGBoost (T) & 1426${}_{-35,+32}$ & 0.264 & 16.8 & 13.7 & 0 & 11.6\% & 637.94 & 0.17 \\
+TabPFNv2 (T) & 1411${}_{-34,+32}$ & 0.393 & 17.3 & 4.9 & 1 & 11.7\% & 3031.01 & 0.59 \\
+TabM (D) & 1400${}_{-27,+26}$ & 0.272 & 18.0 & 11.8 & 0 & 12.9\% & 9.82 & 0.13 \\
+TabPFNv2 (D) & 1391${}_{-38,+28}$ & 0.360 & 18.5 & 4.5 & 4 & 12.7\% & 3.33 & 0.33 \\
+TorchMLP (T+E) & 1381${}_{-30,+28}$ & 0.228 & 18.9 & 14.6 & 0 & 11.9\% & 2372.22 & 2.09 \\
+RealMLP (T) & 1374${}_{-26,+34}$ & 0.198 & 19.2 & 15.0 & 0 & 12.7\% & 5472.30 & 0.17 \\
+EBM (T+E) & 1371${}_{-35,+26}$ & 0.170 & 19.5 & 13.5 & 0 & 15.5\% & 895.61 & 0.20 \\
+FastaiMLP (T+E) & 1347${}_{-32,+32}$ & 0.207 & 20.6 & 12.3 & 0 & 14.9\% & 582.77 & 4.52 \\
+ModernNCA (D) & 1338${}_{-37,+29}$ & 0.142 & 21.1 & 11.9 & 1 & 14.5\% & 14.53 & 0.34 \\
+EBM (T) & 1309${}_{-29,+28}$ & 0.110 & 22.5 & 17.9 & 0 & 16.2\% & 895.61 & 0.02 \\
+EBM (D) & 1274${}_{-27,+35}$ & 0.119 & 24.1 & 11.6 & 1 & 17.0\% & 3.72 & 0.04 \\
+XGBoost (D) & 1266${}_{-30,+28}$ & 0.102 & 24.6 & 19.3 & 0 & 14.6\% & 1.58 & 0.11 \\
+RealMLP (D) & 1262${}_{-24,+34}$ & 0.095 & 24.8 & 20.6 & 0 & 14.7\% & 35.20 & 0.18 \\
+TorchMLP (T) & 1259${}_{-38,+31}$ & 0.085 & 25.0 & 21.6 & 0 & 14.5\% & 2372.22 & 0.15 \\
+TabDPT (D) & 1258${}_{-32,+34}$ & 0.186 & 25.0 & 7.9 & 2 & 15.9\% & 20.51 & 8.54 \\
+ExtraTrees (T+E) & 1254${}_{-30,+32}$ & 0.082 & 25.2 & 18.9 & 0 & 16.5\% & 182.30 & 0.74 \\
+FastaiMLP (T) & 1244${}_{-26,+38}$ & 0.094 & 25.6 & 20.6 & 0 & 16.9\% & 582.77 & 0.29 \\
+RandomForest (T+E) & 1206${}_{-33,+26}$ & 0.067 & 27.5 & 17.4 & 0 & 17.5\% & 260.01 & 0.74 \\
+LightGBM (D) & 1200${}_{-31,+31}$ & 0.053 & 27.7 & 25.8 & 0 & 16.1\% & 1.41 & 0.12 \\
+ExtraTrees (T) & 1196${}_{-36,+29}$ & 0.056 & 27.8 & 23.8 & 0 & 17.9\% & 182.30 & 0.07 \\
+RandomForest (T) & 1153${}_{-34,+27}$ & 0.045 & 29.8 & 22.7 & 0 & 18.7\% & 260.01 & 0.07 \\
+TorchMLP (D) & 1101${}_{-33,+36}$ & 0.022 & 31.8 & 29.0 & 0 & 19.7\% & 6.03 & 0.13 \\
+FastaiMLP (D) & 1070${}_{-35,+35}$ & 0.027 & 32.9 & 29.7 & 0 & 22.3\% & 2.81 & 0.32 \\
+Linear (T+E) & 1029${}_{-33,+28}$ & 0.044 & 34.4 & 24.9 & 0 & 28.0\% & 44.46 & 0.20 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.003 & 35.3 & 33.7 & 0 & 24.4\% & 0.33 & 0.04 \\
+Linear (T) & 991${}_{-29,+34}$ & 0.028 & 35.6 & 30.0 & 0 & 28.9\% & 44.46 & 0.07 \\
+Linear (D) & 984${}_{-37,+30}$ & 0.020 & 35.9 & 27.1 & 0 & 30.0\% & 1.43 & 0.09 \\
+ExtraTrees (D) & 923${}_{-38,+30}$ & 0.011 & 37.5 & 34.5 & 0 & 26.9\% & 0.24 & 0.04 \\
+KNN (T+E) & 686${}_{-48,+31}$ & 0.000 & 41.8 & 41.5 & 0 & 49.7\% & 3.44 & 0.18 \\
+KNN (T) & 597${}_{-47,+39}$ & 0.000 & 42.8 & 42.7 & 0 & 51.5\% & 3.44 & 0.04 \\
+KNN (D) & 410${}_{-78,+56}$ & 0.000 & 44.2 & 44.1 & 0 & 60.0\% & 0.07 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/tabicl-imputed/tabarena_leaderboard.csv b/data/tabicl-imputed/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e24e41bd4a3334eedf84f09b871d0111db4e9ea3
--- /dev/null
+++ b/data/tabicl-imputed/tabarena_leaderboard.csv
@@ -0,0 +1,46 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
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+TABM (tuned + ensemble),32820.849396629244,7.890166007074309,3102.495096524168,1.8344804606248617,0.5092283925039975,0.5350797555443023,0.08573855686320273,0.05963111030471803,48157.60289475875,128.99098784852723,9.694444444444445,0.17235499999999998,8134.35737352901,2.5847251441743637,2387.831479177676,1.4722284599091153,0.47136645123500964,0.5540981099375837,0.03899671266638771,0.02990990162448854,44088.87353980956,121.43882105497326,8.0,3,2,2,29,1588.1,40.9,29.4,0.80239898989899,0.22816337166827363
+TABICL (default),112.87139065964723,20.33866377333064,9.733153451045638,2.345033367232273,0.4788051732855598,0.5342388690836984,0.0733517897000551,0.04928098549249302,178.07934402242574,242.8201765656135,10.652777777777779,0.17207,25.732762111557854,3.564396858215332,8.684246340890724,1.808164283651731,0.4992564187967412,0.567554590317891,0.03487584587193143,0.01673206206271185,151.54793227614402,132.97465082181864,9.5,6,4,1,25,1565.3,32.1,30.1,0.7806186868686869,0.2984959355396537
+REALMLP (tuned + ensemble),78320.7099595922,13.457468075516783,5777.34529914899,4.747200702818298,0.5822217846671746,0.5763475950282718,0.09627181319390925,0.054761231537571176,141412.64388451062,278.6382916983155,11.055555555555555,0.166985,23101.338454975023,5.6439330816268924,5472.29624726055,3.0648418488665286,0.5258237901453162,0.5623887498677591,0.051850568481074444,0.029565742001882556,99490.3161960261,233.98656302124476,9.0,0,0,3,33,1554.6,31.9,37.4,0.7714646464646465,0.13261942492574919
+GBM (tuned + ensemble),2757.3124551578803,12.456019589415302,657.304398009506,2.529125543711048,0.6113071815088934,0.6163296836138819,0.10258436279694044,0.06118717781173281,9019.445489261983,194.08976472235491,11.402777777777779,0.16698000000000002,1484.5727322684393,3.4757837878333198,374.34433555986243,1.4573305804667545,0.6534872634143774,0.6349889770045265,0.051567085599247775,0.02199552512560315,8683.556526223521,104.28918180708692,11.0,1,1,3,31,1546.6,27.1,30.9,0.7635732323232324,0.15604065347685897
+TABM (tuned),32820.849396629244,0.8654038354202553,3102.495096524168,0.19252750021110288,0.5912555585094392,0.5882396070204128,0.09626471764112607,0.0738205060529711,48157.60289475875,13.240850473332038,13.069444444444445,0.174175,8134.35737352901,0.26544706026713055,2387.831479177676,0.1626291486781835,0.5877464635288101,0.634980455939971,0.05532096025125255,0.03209545871697756,44088.87353980956,10.02168824405339,13.75,1,3,2,30,1505.7,31.0,31.9,0.7256944444444444,0.16031899840421437
+CAT (tuned + ensemble),14620.521881076362,2.36174942917294,2269.8595810438947,0.6522846741920234,0.6226459246322733,0.6119512748454302,0.09491452657978199,0.05316377757060419,28484.88552620336,44.38703157771985,13.541666666666666,0.16040500000000002,4812.184866507848,1.2190414004855685,1233.4944988708792,0.5157389806383175,0.6153632608570143,0.669054345099479,0.05899249898577069,0.023358363194257874,20694.132105891145,37.76547447057217,12.5,0,1,2,33,1495.4,27.3,27.9,0.7149621212121212,0.11479127151934615
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+NN_TORCH (default),48.70578088156971,0.5936917389616555,11.125118656665736,0.17673793806000127,0.978257373569646,0.9486699848780762,0.19710882479579594,0.14461232370250393,163.6050788464746,10.908192435522393,31.76388888888889,0.180355,25.35211862458123,0.2432508071263631,6.0317417768089925,0.12717011148259783,1.0,0.996424410496547,0.142508632176782,0.09058250288187904,147.85412694112986,8.619821917518276,33.0,0,0,0,36,1101.4,35.8,33.0,0.30082070707070707,0.034537317016541016
+FASTAI (default),31.09969735491423,1.10650484436824,4.683882685740998,0.49602131438348224,0.972512597016023,0.93827750568811,0.22259183987469472,0.1676085097457016,78.50334203533181,29.20329942994179,32.94444444444444,0.19183499999999998,12.376497785250347,0.7602158255047269,2.8056596594025227,0.32364197153238683,1.0,0.9998853211009175,0.16139709053294848,0.10101015982783891,62.830982755166104,26.31473180904403,36.0,0,0,0,36,1069.9,34.4,34.9,0.273989898989899,0.03368733318328171
+LR (tuned + ensemble),247.81690773551847,1.5417047552120537,86.9527487524539,0.32565721661614494,0.9561054304859822,0.9503142970931224,0.2795482007705742,0.22419398362837153,1145.6529776499187,22.80693330823688,34.416666666666664,0.20382,171.4822693798277,0.2983522944980197,44.45837271402539,0.19711479228248058,1.0,1.0,0.2029910383523567,0.12993275981723412,713.1690352739483,13.25530093456624,37.5,0,0,1,35,1029.4,27.5,32.1,0.24053030303030304,0.0401701082123688
+RF (default),3.0601362299771955,0.1389405298380204,0.4197356009776281,0.0653398591441877,0.9965256868256966,0.9784714305124642,0.24439814041789643,0.23332089200953096,6.380264980433452,3.8863601848700204,35.263888888888886,0.21025,1.1198331514994302,0.08397722244262695,0.3277067685609757,0.03542500892500126,1.0,1.0,0.17586669886220047,0.11692775501298655,5.654732996519019,3.4893450212553976,37.25,0,0,0,36,1000.0,0.0,0.0,0.2212752525252525,0.02965019969409202
+LR (tuned),247.81690773551847,0.4449969635333544,86.9527487524539,0.10396867681281166,0.9723830890370608,0.957603281359881,0.2890832368031623,0.2335985252567378,1145.6529776499187,6.67768937474542,35.55555555555556,0.20357,171.4822693798277,0.11908173561096191,44.45837271402539,0.07001089403664298,1.0,1.0,0.209915797201436,0.13996577713587863,713.1690352739483,4.422167155622043,38.0,0,0,0,36,990.7,33.7,28.9,0.21464646464646464,0.03335309519895627
+LR (default),6.169420995020572,0.45878614010634244,2.106396664251635,0.12050053609932035,0.9795929927195421,0.9635148500480292,0.2995361357732522,0.25915777266480783,29.155480122785303,7.705503358920759,35.861111111111114,0.20875,5.325402127371894,0.12779696782430014,1.4313534079421033,0.0896510462814927,1.0,1.0,0.2098231228117865,0.14283438851684813,21.98095065107635,4.735441569338942,39.5,0,0,1,35,984.0,29.4,36.7,0.2077020202020202,0.036834411850132504
+XT (default),1.8043978815461381,0.17975940917745048,0.3713595805715374,0.06954273446410252,0.9892721532660226,0.9741636720839656,0.2693928274585231,0.26259240866880457,5.400149437442951,4.422216908920569,37.47222222222222,0.21292,0.9855960739983453,0.08559976683722602,0.24198385110028492,0.03905757784211604,1.0,1.0,0.18424442460299273,0.1366333464189622,4.712255061768779,3.9447928104085292,39.0,0,0,0,36,922.9,29.7,37.5,0.1710858585858586,0.028997498234233365
+KNN (tuned + ensemble),167.8303013191547,12.105770299979199,8.709396553002023,0.6510287601474662,1.0,0.9960654060232148,0.49677010357074014,0.6120942109628191,80.7590848163391,80.9066319119832,41.80555555555556,0.31954499999999997,14.630206929312813,0.18374058273103502,3.4367890290794296,0.18360265549810623,1.0,1.0,0.4552264030449861,0.6664113509907352,59.32150817198775,12.487074071232104,43.0,0,0,0,36,685.5,30.3,47.5,0.0726010101010101,0.024106030242002884
+KNN (tuned),167.8303013191547,1.8655599888460135,8.709396553002023,0.10631861792797924,1.0,0.9975847139031918,0.5153228769885266,0.6579120926382002,80.7590848163391,12.961828932017745,42.80555555555556,0.32433,14.630206929312813,0.07810062832302517,3.4367890290794296,0.039402452723266784,1.0,1.0,0.4955223463378593,0.7068928996407389,59.32150817198775,2.342964973116946,44.0,0,0,0,36,596.6,38.1,46.2,0.049873737373737376,0.02344196684773788
+KNN (default),0.8106582238350385,0.22655490967962477,0.11174909219233083,0.031188125017345747,1.0,1.0,0.5999865293650463,0.9596846471567678,1.0058498096362567,2.4347704716843976,44.208333333333336,0.392575,0.22842825783623588,0.035103811158074275,0.06799989431884934,0.01944144969012079,1.0,1.0,0.619455920315494,1.0,1.0,1.2342542936555674,45.0,0,0,0,36,409.8,56.0,77.2,0.017992424242424244,0.0226951547710286
diff --git a/data/tabicl-imputed/tuning-impact-elo-horizontal.pdf b/data/tabicl-imputed/tuning-impact-elo-horizontal.pdf
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diff --git a/data/tabicl-imputed/tuning-impact-elo-horizontal.png.zip b/data/tabicl-imputed/tuning-impact-elo-horizontal.png.zip
new file mode 100644
index 0000000000000000000000000000000000000000..239598955be83ce2de1491934bc83495b5eb0560
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+version https://git-lfs.github.com/spec/v1
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+size 152921
diff --git a/data/tabicl/figures/critical-diagram.pdf b/data/tabicl/figures/critical-diagram.pdf
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+version https://git-lfs.github.com/spec/v1
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diff --git a/data/tabicl/leaderboard.tex b/data/tabicl/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..f6a356299f52996338ff47cc4a594c8f45ef23e4
--- /dev/null
+++ b/data/tabicl/leaderboard.tex
@@ -0,0 +1,50 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+AutoGluon 1.3 (4h) & \textcolor{gold}{\textbf{1645${}_{-36,+32}$}} & \textcolor{gold}{\textbf{0.569}} & \textcolor{gold}{\textbf{7.9}} & \textcolor{silver}{\textbf{2.9}} & \textcolor{silver}{\textbf{7}} & \textcolor{gold}{\textbf{5.4\%}} & 1222.55 & 2.36 \\
+TabM (T+E) & \textcolor{silver}{\textbf{1634${}_{-32,+36}$}} & \textcolor{bronze}{\textbf{0.491}} & \textcolor{silver}{\textbf{8.3}} & \textcolor{bronze}{\textbf{3.4}} & \textcolor{bronze}{\textbf{4}} & \textcolor{bronze}{\textbf{6.7\%}} & 2387.83 & 1.47 \\
+TabICL (D) & \textcolor{bronze}{\textbf{1599${}_{-40,+31}$}} & \textcolor{silver}{\textbf{0.521}} & \textcolor{bronze}{\textbf{9.4}} & \textcolor{gold}{\textbf{2.8}} & \textcolor{gold}{\textbf{8}} & \textcolor{gold}{\textbf{5.4\%}} & 8.68 & 1.81 \\
+RealMLP (T+E) & 1598${}_{-34,+29}$ & 0.418 & 9.5 & 5.5 & 1 & 8.1\% & 5472.30 & 3.06 \\
+LightGBM (T+E) & 1584${}_{-23,+33}$ & 0.389 & 9.9 & 5.7 & 1 & 8.8\% & 374.34 & 1.46 \\
+TabM (T) & 1537${}_{-32,+35}$ & 0.409 & 11.6 & 5.4 & 1 & 7.8\% & 2387.83 & 0.16 \\
+CatBoost (T+E) & 1530${}_{-29,+28}$ & 0.377 & 11.9 & 7.0 & 1 & 7.7\% & 1233.49 & 0.52 \\
+CatBoost (T) & 1522${}_{-27,+30}$ & 0.360 & 12.3 & 6.3 & 1 & 7.9\% & 1233.49 & 0.07 \\
+LightGBM (T) & 1504${}_{-26,+33}$ & 0.302 & 13.0 & 10.9 & 0 & 9.5\% & 374.34 & 0.22 \\
+CatBoost (D) & 1502${}_{-25,+23}$ & 0.343 & 13.0 & 6.4 & 1 & 9.2\% & 5.31 & 0.07 \\
+XGBoost (T+E) & 1497${}_{-31,+30}$ & 0.320 & 13.2 & 9.3 & 0 & 9.6\% & 637.94 & 1.22 \\
+ModernNCA (T) & 1462${}_{-31,+33}$ & 0.275 & 14.7 & 7.1 & 2 & 9.2\% & 4614.64 & 0.52 \\
+ModernNCA (T+E) & 1455${}_{-33,+30}$ & 0.383 & 15.0 & 6.0 & 1 & 9.1\% & 4614.64 & 8.40 \\
+XGBoost (T) & 1450${}_{-30,+33}$ & 0.264 & 15.0 & 12.4 & 0 & 10.0\% & 637.94 & 0.17 \\
+TabM (D) & 1425${}_{-27,+31}$ & 0.272 & 16.3 & 10.0 & 0 & 11.5\% & 9.82 & 0.13 \\
+TorchMLP (T+E) & 1401${}_{-23,+28}$ & 0.228 & 17.1 & 12.2 & 0 & 10.5\% & 2372.22 & 2.09 \\
+RealMLP (T) & 1399${}_{-38,+31}$ & 0.198 & 17.4 & 12.6 & 0 & 11.3\% & 5472.30 & 0.17 \\
+EBM (T+E) & 1391${}_{-27,+26}$ & 0.170 & 17.7 & 10.7 & 0 & 13.9\% & 895.61 & 0.20 \\
+FastaiMLP (T+E) & 1367${}_{-30,+31}$ & 0.207 & 18.8 & 10.6 & 0 & 13.9\% & 582.77 & 4.52 \\
+ModernNCA (D) & 1355${}_{-26,+29}$ & 0.142 & 19.3 & 10.4 & 1 & 13.1\% & 14.53 & 0.34 \\
+EBM (T) & 1328${}_{-36,+26}$ & 0.110 & 20.6 & 15.1 & 0 & 14.7\% & 895.61 & 0.02 \\
+EBM (D) & 1292${}_{-29,+29}$ & 0.119 & 22.1 & 7.9 & 3 & 15.6\% & 3.72 & 0.04 \\
+XGBoost (D) & 1277${}_{-22,+34}$ & 0.102 & 22.6 & 18.0 & 0 & 13.2\% & 1.58 & 0.11 \\
+RealMLP (D) & 1274${}_{-30,+27}$ & 0.095 & 22.9 & 18.8 & 0 & 13.3\% & 35.20 & 0.18 \\
+TabDPT (D) & 1269${}_{-30,+35}$ & 0.186 & 23.0 & 6.2 & 3 & 14.7\% & 20.51 & 8.54 \\
+TorchMLP (T) & 1269${}_{-22,+32}$ & 0.085 & 23.0 & 19.1 & 0 & 13.2\% & 2372.22 & 0.15 \\
+ExtraTrees (T+E) & 1263${}_{-28,+29}$ & 0.082 & 23.3 & 15.9 & 0 & 15.2\% & 182.30 & 0.74 \\
+FastaiMLP (T) & 1259${}_{-29,+29}$ & 0.094 & 23.5 & 18.2 & 0 & 16.0\% & 582.77 & 0.29 \\
+RandomForest (T+E) & 1210${}_{-24,+31}$ & 0.067 & 25.5 & 15.4 & 0 & 16.0\% & 260.01 & 0.74 \\
+LightGBM (D) & 1206${}_{-25,+30}$ & 0.053 & 25.7 & 23.9 & 0 & 14.8\% & 1.41 & 0.12 \\
+ExtraTrees (T) & 1205${}_{-27,+30}$ & 0.056 & 25.8 & 20.7 & 0 & 16.7\% & 182.30 & 0.07 \\
+RandomForest (T) & 1156${}_{-28,+29}$ & 0.045 & 27.7 & 21.1 & 0 & 17.3\% & 260.01 & 0.07 \\
+TorchMLP (D) & 1103${}_{-32,+34}$ & 0.022 & 29.6 & 26.8 & 0 & 18.6\% & 6.03 & 0.13 \\
+FastaiMLP (D) & 1073${}_{-31,+34}$ & 0.027 & 30.6 & 27.5 & 0 & 21.5\% & 2.81 & 0.32 \\
+Linear (T+E) & 1030${}_{-39,+33}$ & 0.044 & 32.0 & 23.1 & 0 & 26.9\% & 44.46 & 0.20 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.003 & 32.8 & 31.3 & 0 & 23.4\% & 0.33 & 0.04 \\
+Linear (T) & 989${}_{-24,+35}$ & 0.028 & 33.1 & 26.3 & 0 & 27.9\% & 44.46 & 0.07 \\
+Linear (D) & 979${}_{-29,+35}$ & 0.020 & 33.4 & 17.4 & 1 & 29.2\% & 1.43 & 0.09 \\
+ExtraTrees (D) & 929${}_{-38,+35}$ & 0.011 & 34.7 & 31.5 & 0 & 26.1\% & 0.24 & 0.04 \\
+KNN (T+E) & 694${}_{-47,+39}$ & 0.000 & 38.8 & 38.5 & 0 & 49.2\% & 3.44 & 0.18 \\
+KNN (T) & 597${}_{-48,+54}$ & 0.000 & 39.8 & 39.7 & 0 & 51.1\% & 3.44 & 0.04 \\
+KNN (D) & 407${}_{-77,+83}$ & 0.000 & 41.2 & 41.1 & 0 & 59.7\% & 0.07 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/tabicl/tabarena_leaderboard.csv b/data/tabicl/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9ff26eed457798f850c0be6a353f123ed8bd3afa
--- /dev/null
+++ b/data/tabicl/tabarena_leaderboard.csv
@@ -0,0 +1,43 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+AutoGluon 1.3 (4h),7750.377155031171,22.84719785201697,2751.5674214709147,2.9633558383567813,0.4305752039617605,0.3953654066045534,0.05350042752511665,0.03292137769247373,32689.64667720609,276.28827431069953,7.916666666666667,0.159155,6752.988276031282,3.4718479580349393,1222.5529738029095,2.35500290690449,0.3593825434326649,0.3493492611774055,0.022091856756724426,0.014502276761886618,26140.06177622487,154.15434639040092,5.0,7,3,2,24,1645.2,31.5,36.0,0.8313008130081301,0.34643609213236404
+TABM (tuned + ensemble),32820.849396629244,7.890166007074309,3102.495096524168,1.8344804606248617,0.5092283925039975,0.4916904001038966,0.06741513792808897,0.0531492276149925,48157.60289475875,128.99098784852723,8.277777777777779,0.17235499999999998,8134.35737352901,2.5847251441743637,2387.831479177676,1.4722284599091153,0.47136645123500964,0.47309371248601884,0.032755260863256086,0.01377708489439205,44088.87353980956,121.43882105497326,5.0,4,6,3,23,1634.2,35.1,31.2,0.8224932249322493,0.29539017627252917
+TABICL (default),112.87139065964723,20.33866377333064,9.733153451045638,2.345033367232273,0.4788051732855598,0.4999558134896783,0.05432482602183145,0.042761048439141636,178.07934402242574,242.8201765656135,9.430555555555555,0.17207,25.732762111557854,3.564396858215332,8.684246340890724,1.808164283651731,0.4992564187967412,0.5554459288972347,0.022981096428129733,0.00939871045261338,151.54793227614402,132.97465082181864,7.0,8,5,1,22,1598.7,30.5,39.9,0.7943766937669376,0.35893420945385096
+REALMLP (tuned + ensemble),78320.7099595922,13.457468075516783,5777.34529914899,4.747200702818298,0.5822217846671746,0.5363650919569951,0.08100170596576858,0.048333408990573846,141412.64388451062,278.6382916983155,9.458333333333334,0.166985,23101.338454975023,5.6439330816268924,5472.29624726055,3.0648418488665286,0.5258237901453162,0.5461812667291126,0.044731996378166494,0.02214701961011789,99490.3161960261,233.98656302124476,8.0,1,1,5,29,1597.7,28.7,33.8,0.7936991869918699,0.18239141458573893
+GBM (tuned + ensemble),2757.3124551578803,12.456019589415302,657.304398009506,2.529125543711048,0.6113071815088934,0.5862746617157221,0.08764117401144472,0.05490427588548946,9019.445489261983,194.08976472235491,9.875,0.16698000000000002,1484.5727322684393,3.4757837878333198,374.34433555986243,1.4573305804667545,0.6534872634143774,0.6218047916068159,0.048374130042081465,0.01740708281579771,8683.556526223521,104.28918180708692,8.5,1,3,1,31,1584.3,32.8,22.7,0.7835365853658537,0.17646098963500922
+TABM (tuned),32820.849396629244,0.8654038354202553,3102.495096524168,0.19252750021110288,0.5912555585094392,0.5498665833450093,0.07844798244845448,0.06744705276522203,48157.60289475875,13.240850473332038,11.569444444444445,0.174175,8134.35737352901,0.26544706026713055,2387.831479177676,0.1626291486781835,0.5877464635288101,0.5841370688586468,0.037275782250363654,0.01907334143513442,44088.87353980956,10.02168824405339,12.0,1,4,3,28,1536.9,34.6,31.2,0.7422086720867209,0.18618211258130268
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+CAT (tuned),14620.521881076362,0.4177007556697469,2269.8595810438947,0.10130234094803403,0.6397756322608608,0.595921365819077,0.07946152837678017,0.046679526112002975,28484.88552620336,6.396687248681741,12.319444444444445,0.161705,4812.184866507848,0.12577376100752088,1233.4944988708792,0.07246540449837217,0.6663747291561838,0.6619777725537788,0.04785266987647563,0.026129509718822636,20694.132105891145,5.269306492183282,12.25,1,3,1,31,1521.7,29.1,26.2,0.7239159891598916,0.15993342501015134
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+XT (default),1.8043978815461381,0.17975940917745048,0.3713595805715374,0.06954273446410252,0.9892721532660226,0.9709853179280655,0.2609714953848282,0.2584310419389792,5.400149437442951,4.422216908920569,34.69444444444444,0.21292,0.9855960739983453,0.08559976683722602,0.24198385110028492,0.03905757784211604,1.0,1.0,0.18424442460299273,0.13284238574591212,4.712255061768779,3.9447928104085292,36.0,0,0,0,36,928.7,34.2,37.2,0.17818428184281843,0.031705419003530376
+KNN (tuned + ensemble),167.8303013191547,12.105770299979199,8.709396553002023,0.6510287601474662,1.0,0.9950381724620512,0.4922467034154276,0.6086097785091938,80.7590848163391,80.9066319119832,38.84722222222222,0.31954499999999997,14.630206929312813,0.18374058273103502,3.4367890290794296,0.18360265549810623,1.0,1.0,0.4552264030449861,0.6664113509907352,59.32150817198775,12.487074071232104,40.0,0,0,0,36,694.5,38.1,46.7,0.0768970189701897,0.025970231106033758
+KNN (tuned),167.8303013191547,1.8655599888460135,8.709396553002023,0.10631861792797924,1.0,0.9973606072760356,0.5109632324100885,0.654584490441368,80.7590848163391,12.961828932017745,39.84722222222222,0.32433,14.630206929312813,0.07810062832302517,3.4367890290794296,0.039402452723266784,1.0,1.0,0.4955223463378593,0.7068928996407389,59.32150817198775,2.342964973116946,41.0,0,0,0,36,596.8,53.8,47.6,0.05250677506775068,0.02518666084837813
+KNN (default),0.8106582238350385,0.22655490967962477,0.11174909219233083,0.031188125017345747,1.0,1.0,0.5965726261878597,0.9589083406549634,1.0058498096362567,2.4347704716843976,41.25,0.392575,0.22842825783623588,0.035103811158074275,0.06799989431884934,0.01944144969012079,1.0,1.0,0.6183072807462191,1.0,1.0,1.2342542936555674,42.0,0,0,0,36,406.8,82.2,76.6,0.018292682926829267,0.02432448428372201
diff --git a/data/tabicl/tuning-impact-elo-horizontal.pdf b/data/tabicl/tuning-impact-elo-horizontal.pdf
new file mode 100644
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diff --git a/data/tabicl/tuning-impact-elo-horizontal.png.zip b/data/tabicl/tuning-impact-elo-horizontal.png.zip
new file mode 100644
index 0000000000000000000000000000000000000000..9ba326e93cb3d1dedb1b9ee2b47b2ff585feedb3
--- /dev/null
+++ b/data/tabicl/tuning-impact-elo-horizontal.png.zip
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e858c95af701d1a8d5d6368f1a8c3669f399c2229854ccff0595e69c3ea3da0d
+size 132739
diff --git a/data/tabpfn-cls/figures/critical-diagram.pdf b/data/tabpfn-cls/figures/critical-diagram.pdf
new file mode 100644
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new file mode 100644
index 0000000000000000000000000000000000000000..c621877d3cafbab56387efc63b4f34b1b84d9942
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+++ b/data/tabpfn-cls/figures/critical-diagram.png.zip
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+version https://git-lfs.github.com/spec/v1
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diff --git a/data/tabpfn-cls/leaderboard.tex b/data/tabpfn-cls/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..35e6057280fc43f5c059298d0300adb0d291476f
--- /dev/null
+++ b/data/tabpfn-cls/leaderboard.tex
@@ -0,0 +1,52 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+TabPFNv2 (T+E) & \textcolor{gold}{\textbf{1730${}_{-37,+55}$}} & \textcolor{gold}{\textbf{0.700}} & \textcolor{gold}{\textbf{5.4}} & \textcolor{gold}{\textbf{2.0}} & \textcolor{gold}{\textbf{9}} & \textcolor{gold}{\textbf{4.1\%}} & 3445.60 & 48.24 \\
+TabM (T+E) & \textcolor{silver}{\textbf{1613${}_{-39,+44}$}} & 0.483 & \textcolor{silver}{\textbf{8.7}} & 4.2 & \textcolor{bronze}{\textbf{2}} & \textcolor{bronze}{\textbf{8.7\%}} & 2828.45 & 1.60 \\
+TabPFNv2 (T) & \textcolor{bronze}{\textbf{1576${}_{-30,+46}$}} & \textcolor{silver}{\textbf{0.544}} & \textcolor{bronze}{\textbf{9.9}} & \textcolor{bronze}{\textbf{3.5}} & 1 & \textcolor{silver}{\textbf{7.7\%}} & 3445.60 & 1.00 \\
+RealMLP (T+E) & 1568${}_{-29,+35}$ & 0.402 & 10.3 & 6.5 & 0 & 9.8\% & 5786.69 & 4.27 \\
+AutoGluon 1.3 (4h) & 1541${}_{-35,+42}$ & 0.440 & 11.3 & 4.9 & \textcolor{bronze}{\textbf{2}} & 8.9\% & 2309.21 & 2.55 \\
+TabPFNv2 (D) & 1540${}_{-42,+45}$ & \textcolor{bronze}{\textbf{0.499}} & 11.5 & \textcolor{silver}{\textbf{3.1}} & \textcolor{silver}{\textbf{5}} & 9.0\% & 4.06 & 0.44 \\
+LightGBM (T+E) & 1516${}_{-28,+51}$ & 0.319 & 12.2 & 6.9 & 1 & 11.2\% & 647.56 & 1.72 \\
+TabM (T) & 1510${}_{-31,+38}$ & 0.387 & 12.6 & 7.1 & 0 & 9.9\% & 2828.45 & 0.22 \\
+LightGBM (T) & 1463${}_{-33,+36}$ & 0.262 & 14.6 & 12.6 & 0 & 11.6\% & 647.56 & 0.28 \\
+CatBoost (T+E) & 1453${}_{-31,+33}$ & 0.271 & 15.1 & 11.2 & 0 & 11.1\% & 1465.86 & 0.69 \\
+CatBoost (T) & 1434${}_{-34,+48}$ & 0.247 & 15.8 & 9.9 & 0 & 11.4\% & 1465.86 & 0.09 \\
+CatBoost (D) & 1424${}_{-35,+44}$ & 0.228 & 16.3 & 8.2 & 1 & 12.8\% & 5.72 & 0.11 \\
+ModernNCA (T) & 1422${}_{-23,+43}$ & 0.255 & 16.3 & 8.3 & 1 & 11.1\% & 5944.88 & 0.52 \\
+TabM (D) & 1420${}_{-31,+39}$ & 0.288 & 16.3 & 10.9 & 0 & 13.2\% & 10.42 & 0.15 \\
+XGBoost (T+E) & 1412${}_{-30,+40}$ & 0.222 & 16.7 & 13.2 & 0 & 12.6\% & 766.06 & 1.92 \\
+EBM (T+E) & 1381${}_{-33,+33}$ & 0.186 & 18.3 & 12.0 & 0 & 15.2\% & 1109.06 & 0.23 \\
+XGBoost (T) & 1378${}_{-30,+36}$ & 0.181 & 18.4 & 16.1 & 0 & 12.8\% & 766.06 & 0.28 \\
+ModernNCA (T+E) & 1374${}_{-37,+43}$ & 0.307 & 18.7 & 7.7 & 0 & 12.2\% & 5944.88 & 8.40 \\
+RealMLP (T) & 1372${}_{-32,+39}$ & 0.201 & 18.6 & 14.3 & 0 & 12.9\% & 5786.69 & 0.25 \\
+ModernNCA (D) & 1366${}_{-28,+41}$ & 0.183 & 18.9 & 9.5 & 1 & 14.6\% & 14.80 & 0.34 \\
+TorchMLP (T+E) & 1356${}_{-35,+37}$ & 0.199 & 19.4 & 13.9 & 0 & 12.8\% & 2862.05 & 2.16 \\
+FastaiMLP (T+E) & 1354${}_{-36,+44}$ & 0.230 & 19.5 & 10.9 & 0 & 15.1\% & 1358.63 & 8.07 \\
+TabDPT (D) & 1318${}_{-38,+39}$ & 0.258 & 21.2 & 5.6 & \textcolor{bronze}{\textbf{2}} & 14.3\% & 27.49 & 8.86 \\
+EBM (T) & 1316${}_{-33,+30}$ & 0.120 & 21.4 & 16.7 & 0 & 16.0\% & 1109.06 & 0.03 \\
+EBM (D) & 1302${}_{-45,+29}$ & 0.153 & 22.2 & 9.5 & 1 & 16.7\% & 5.28 & 0.08 \\
+FastaiMLP (T) & 1265${}_{-36,+37}$ & 0.113 & 23.6 & 18.5 & 0 & 16.7\% & 1358.63 & 0.90 \\
+ExtraTrees (T+E) & 1260${}_{-45,+41}$ & 0.102 & 24.0 & 16.0 & 0 & 17.7\% & 370.85 & 1.47 \\
+TorchMLP (T) & 1239${}_{-32,+48}$ & 0.086 & 24.9 & 20.9 & 0 & 15.4\% & 2862.05 & 0.15 \\
+RealMLP (D) & 1232${}_{-33,+38}$ & 0.083 & 25.1 & 21.0 & 0 & 15.4\% & 36.24 & 0.28 \\
+XGBoost (D) & 1212${}_{-31,+32}$ & 0.032 & 26.1 & 23.8 & 0 & 16.2\% & 2.40 & 0.22 \\
+ExtraTrees (T) & 1201${}_{-36,+42}$ & 0.072 & 26.5 & 21.5 & 0 & 18.9\% & 370.85 & 0.16 \\
+RandomForest (T+E) & 1156${}_{-35,+40}$ & 0.057 & 28.5 & 20.9 & 0 & 19.0\% & 527.42 & 1.39 \\
+LightGBM (D) & 1152${}_{-34,+31}$ & 0.030 & 28.6 & 26.7 & 0 & 17.2\% & 2.90 & 0.13 \\
+RandomForest (T) & 1095${}_{-33,+30}$ & 0.023 & 30.9 & 27.9 & 0 & 20.3\% & 527.42 & 0.12 \\
+TorchMLP (D) & 1086${}_{-40,+33}$ & 0.030 & 31.4 & 27.9 & 0 & 20.9\% & 10.38 & 0.19 \\
+FastaiMLP (D) & 1045${}_{-43,+44}$ & 0.019 & 32.7 & 30.1 & 0 & 22.5\% & 4.73 & 0.62 \\
+Linear (T+E) & 1028${}_{-38,+37}$ & 0.055 & 33.3 & 22.1 & 0 & 27.9\% & 88.63 & 0.26 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.005 & 34.3 & 32.3 & 0 & 25.3\% & 0.45 & 0.07 \\
+Linear (T) & 995${}_{-43,+52}$ & 0.035 & 34.2 & 26.9 & 0 & 28.6\% & 88.63 & 0.09 \\
+Linear (D) & 987${}_{-47,+35}$ & 0.025 & 34.7 & 24.5 & 0 & 30.1\% & 2.27 & 0.11 \\
+ExtraTrees (D) & 948${}_{-42,+37}$ & 0.015 & 35.7 & 31.7 & 0 & 27.1\% & 0.40 & 0.07 \\
+KNN (T+E) & 709${}_{-55,+44}$ & 0.000 & 40.6 & 40.2 & 0 & 47.6\% & 3.71 & 0.17 \\
+KNN (T) & 607${}_{-58,+48}$ & 0.000 & 41.7 & 41.5 & 0 & 49.7\% & 3.71 & 0.04 \\
+KNN (D) & 404${}_{-91,+71}$ & 0.000 & 43.2 & 43.1 & 0 & 58.9\% & 0.07 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/tabpfn-cls/tabarena_leaderboard.csv b/data/tabpfn-cls/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..341da3321da137fc4baf9ec8a0416700852d8104
--- /dev/null
+++ b/data/tabpfn-cls/tabarena_leaderboard.csv
@@ -0,0 +1,45 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
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+TABM (tuned + ensemble),7080.034926002046,2.290320276704609,3436.5218404284533,2.1079046445931953,0.5166525238887076,0.527521976123192,0.086800842708324,0.043258299937220426,43123.835967101855,114.22499982092596,8.73076923076923,0.17379499999999998,6405.6722231189415,1.880999751885732,2828.4486752645407,1.5957955528425738,0.47136645123500964,0.5421040878145871,0.02983493982634744,0.030759586434290852,44088.87353980956,109.46959390379024,7.5,2,2,2,20,1613.1,43.3,38.8,0.8202146690518783,0.2396584139862172
+TABPFNV2 (tuned),14614.639715819583,4.849548537201351,3877.419600731425,2.254088269272259,0.4562783530638654,0.475811679601056,0.0766403575120324,0.0442810758124181,72438.91792383758,165.31166707386078,9.903846153846153,0.1868,8173.816975453165,0.8871031337314181,3445.602606086448,0.9952991538273057,0.34109768962419973,0.4593895408835382,0.05280536936566166,0.02327176321840322,43032.16406688419,48.819771718398094,6.0,1,9,1,15,1576.1,45.8,29.6,0.7929338103756708,0.2823376982617133
+REALMLP (tuned + ensemble),17809.543566583365,5.343982117807764,5995.031437071161,5.920653730744878,0.5983187605703805,0.5678322254877531,0.09839564063585614,0.04590879055951824,112219.73072981052,290.95193637081104,10.26923076923077,0.17424,10344.939689996507,4.694504770967695,5786.691668992368,4.267078452745884,0.5696681015084324,0.5465586836857503,0.04527754696701686,0.029565742001882556,87871.6182808383,267.1363813998378,8.5,0,1,2,23,1567.9,34.8,28.4,0.7844364937388193,0.15429723445201868
+AutoGluon 1.3 (4h),5931.009838958377,3.1656774007357082,3673.8246676849526,3.0250106370782723,0.5599854226187498,0.5337145991261737,0.08907011998592504,0.04615933213503606,39712.28933278582,151.42916243848958,11.307692307692308,0.17691,5020.2655313505065,2.664059625731574,2309.213100138395,2.5543334551748154,0.5600808158121042,0.5584517906521018,0.03780698624823603,0.02506476981872757,35357.34871646478,128.98489015193604,8.0,2,1,1,22,1540.7,41.5,34.3,0.7602862254025045,0.20613108296931826
+TABPFNV2 (default),11.884418644151115,1.1739113624279316,5.514520630749067,0.6490305586450037,0.5013642769155608,0.5568562487011248,0.09004575912430655,0.05754087492207787,76.29505964660768,41.758850754001486,11.5,0.1886,8.093020015292698,0.44081105126274955,4.062007578219401,0.4356807523460313,0.3771347187714357,0.5208009960396708,0.04756401740069338,0.01844263956439219,58.62232186481185,25.39697585954705,5.0,5,0,4,17,1539.6,44.8,41.4,0.7558139534883721,0.31863358201013
+GBM (tuned + ensemble),1379.7589400635827,3.041326452829899,860.8328230742596,2.952036385308545,0.6806482110513491,0.6645460444945642,0.11216818462718134,0.05545122740079456,10455.248725429481,156.11534656534064,12.25,0.174085,1321.8940714677174,2.4085231754514904,647.5566852470606,1.7204157994123173,0.7005535661954161,0.6945287029937819,0.04657388172920551,0.03466366526779107,9480.825611975059,103.35126359750211,11.5,1,0,2,23,1516.5,50.5,27.3,0.7383720930232558,0.14542288002842635
+TABM (tuned),7080.034926002046,0.2169825582422762,3436.5218404284533,0.22059896519012676,0.6127855914191946,0.5828601382328022,0.0986473816088485,0.05699936952152234,43123.835967101855,11.305450805467302,12.615384615384615,0.17596499999999998,6405.6722231189415,0.19745506313112046,2828.4486752645407,0.21564835018398254,0.5915500043256848,0.6198210550262315,0.040413461666905515,0.03591570011140373,44088.87353980956,9.589800419015791,12.0,0,2,1,23,1510.5,37.4,30.1,0.7298747763864043,0.14075728057706524
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+CAT (default),184.70535022315818,0.14151572040003588,154.6142383995702,0.1555426094856078,0.7721520925382083,0.7558591506768357,0.127512601975712,0.06848520945739753,477.2608484422213,7.423108461919998,16.307692307692307,0.176625,11.6375067697631,0.16093741522894967,5.723546572951673,0.1101036800878527,0.8708469956136012,0.8157510033149784,0.06821441929234145,0.03610517615857824,107.22813859237792,5.910285969899903,16.5,1,1,0,24,1423.6,43.5,34.5,0.6440071556350626,0.12237694076494982
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+XT (tuned + ensemble),714.8477392964893,1.554570463987497,476.1600651525857,1.7304383155841179,0.8984673764067701,0.8564702160947721,0.17712340098435098,0.11828689054002459,6037.128774148354,86.31947081771706,24.03846153846154,0.18718,684.9222148127026,1.3082488920953539,370.85408017752667,1.4664534567412004,1.0,0.9269961626847824,0.09673248729372463,0.07108643848262665,5339.627074654447,77.0823275943278,27.25,0,0,0,26,1259.5,40.3,44.2,0.46422182468694095,0.0626754356821208
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+RF (default),0.9640304686676744,0.07426402477117686,0.5084719578868067,0.0844146006139,0.9951894125278877,0.9674741068203553,0.25319346313286606,0.2448343620876502,6.6754175478812146,4.153857105918795,34.28846153846154,0.213105,0.8910642200046115,0.05825435982810126,0.447836563000179,0.06594795127086661,1.0,1.0,0.17586669886220047,0.11508233454921707,5.654732996519019,3.502947097147378,36.5,0,0,0,26,1000.0,0.0,0.0,0.22584973166368516,0.030931987921747967
+LR (default),4.707631549162742,0.1483236100938585,2.804518891637732,0.1437661673750307,0.9745259999639975,0.9505629021800448,0.3011073713268812,0.28932043078797587,36.73103788870433,7.308777223189359,34.71153846153846,0.22103499999999998,4.746849238872528,0.08430976470311483,2.2657084486770183,0.10642460584640503,1.0,1.0,0.22353997609762993,0.22370793213879017,31.50980214431158,4.703818974402271,38.5,0,0,1,25,987.0,34.2,46.6,0.21601073345259392,0.040796023063587754
+XT (default),0.8462119445841536,0.0803421718442542,0.47456795510527094,0.08795126020679146,0.9851460583683391,0.9609016392969725,0.27096026301478127,0.27365502000930814,6.0304600748852675,4.406716139948246,35.65384615384615,0.215405,0.7635703219307794,0.06673479080200195,0.40395335630596185,0.07007418015238884,1.0,1.0,0.18129521821713152,0.14553182686734578,5.148113375558061,3.7424721126192138,38.0,0,0,0,26,948.4,36.6,41.3,0.19409660107334525,0.03158544652624897
+KNN (tuned + ensemble),16.440937072497146,0.2739423143558013,7.036183733958932,0.19964653999651732,1.0,0.9942158538470305,0.4761836733018972,0.5893872313062054,57.836601086323085,11.285407568076662,40.57692307692308,0.31118999999999997,6.799899099932777,0.1030390567249722,3.707274221912526,0.16730320161416595,1.0,1.0,0.4357561775644644,0.6183873456591064,55.06974575171796,8.266636963631449,42.0,0,0,0,26,708.8,43.4,54.7,0.07960644007155634,0.02489112819208369
+KNN (tuned),16.440937072497146,0.06075953357240074,7.036183733958932,0.04304950843110991,1.0,0.9966827264588594,0.4973658893922803,0.6435244474284593,57.836601086323085,2.5027008595459077,41.69230769230769,0.31467999999999996,6.799899099932777,0.036990099483066134,3.707274221912526,0.039402452723266784,1.0,1.0,0.4955223463378593,0.6729856958219811,55.06974575171796,2.091430487051362,43.0,0,0,0,26,606.6,47.2,57.4,0.05366726296958855,0.024070134689831643
+KNN (default),0.2899092884145231,0.0317453768518236,0.13426353967394633,0.031065835648244968,1.0,1.0,0.5891670346049616,0.9490733069136094,1.00065410372272,1.4527699655062962,43.25,0.34582,0.1258228341738383,0.02117468251122369,0.07457399441809831,0.021006283652748647,1.0,1.0,0.6134116269473473,1.0,1.0,1.0057335151210618,44.0,0,0,0,26,404.4,70.3,90.7,0.01744186046511628,0.02319085778775572
diff --git a/data/tabpfn-cls/tuning-impact-elo-horizontal.pdf b/data/tabpfn-cls/tuning-impact-elo-horizontal.pdf
new file mode 100644
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diff --git a/data/tabpfn-cls/tuning-impact-elo-horizontal.png.zip b/data/tabpfn-cls/tuning-impact-elo-horizontal.png.zip
new file mode 100644
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diff --git a/data/tabpfn-imputed-cls/figures/critical-diagram.pdf b/data/tabpfn-imputed-cls/figures/critical-diagram.pdf
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+version https://git-lfs.github.com/spec/v1
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diff --git a/data/tabpfn-imputed-cls/leaderboard.tex b/data/tabpfn-imputed-cls/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..b41ff65b5428b5f7a450717fe825914a05f0b501
--- /dev/null
+++ b/data/tabpfn-imputed-cls/leaderboard.tex
@@ -0,0 +1,53 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+TabPFNv2 (T+E) & \textcolor{gold}{\textbf{1736${}_{-45,+46}$}} & \textcolor{gold}{\textbf{0.700}} & \textcolor{gold}{\textbf{5.8}} & \textcolor{gold}{\textbf{2.2}} & \textcolor{gold}{\textbf{8}} & \textcolor{gold}{\textbf{4.6\%}} & 3445.60 & 48.24 \\
+TabM (T+E) & \textcolor{silver}{\textbf{1612${}_{-37,+35}$}} & 0.483 & \textcolor{silver}{\textbf{9.3}} & 4.5 & 2 & 9.2\% & 2828.45 & 1.60 \\
+TabICL (D) & \textcolor{bronze}{\textbf{1608${}_{-34,+36}$}} & \textcolor{bronze}{\textbf{0.543}} & \textcolor{bronze}{\textbf{9.5}} & \textcolor{silver}{\textbf{3.3}} & \textcolor{silver}{\textbf{4}} & \textcolor{silver}{\textbf{7.1\%}} & 8.89 & 1.74 \\
+TabPFNv2 (T) & 1588${}_{-42,+32}$ & \textcolor{silver}{\textbf{0.544}} & 10.3 & 3.7 & 1 & \textcolor{bronze}{\textbf{8.2\%}} & 3445.60 & 1.00 \\
+RealMLP (T+E) & 1573${}_{-37,+29}$ & 0.402 & 10.9 & 7.1 & 0 & 10.4\% & 5786.69 & 4.27 \\
+AutoGluon 1.3 (4h) & 1544${}_{-44,+34}$ & 0.440 & 11.9 & 5.6 & 1 & 9.5\% & 2309.21 & 2.55 \\
+TabPFNv2 (D) & 1542${}_{-38,+38}$ & 0.499 & 12.0 & \textcolor{bronze}{\textbf{3.4}} & \textcolor{silver}{\textbf{4}} & 9.5\% & 4.06 & 0.44 \\
+LightGBM (T+E) & 1521${}_{-35,+40}$ & 0.319 & 12.9 & 7.1 & 1 & 11.8\% & 647.56 & 1.72 \\
+TabM (T) & 1512${}_{-40,+30}$ & 0.387 & 13.3 & 7.5 & 0 & 10.4\% & 2828.45 & 0.22 \\
+LightGBM (T) & 1462${}_{-34,+33}$ & 0.262 & 15.3 & 13.3 & 0 & 12.2\% & 647.56 & 0.28 \\
+CatBoost (T+E) & 1456${}_{-32,+38}$ & 0.271 & 15.8 & 11.6 & 0 & 11.7\% & 1465.86 & 0.69 \\
+CatBoost (T) & 1435${}_{-32,+36}$ & 0.247 & 16.5 & 10.5 & 0 & 12.0\% & 1465.86 & 0.09 \\
+TabM (D) & 1429${}_{-32,+35}$ & 0.288 & 17.0 & 11.2 & 0 & 13.7\% & 10.42 & 0.15 \\
+ModernNCA (T) & 1426${}_{-30,+37}$ & 0.255 & 17.1 & 8.7 & 1 & 11.7\% & 5944.88 & 0.52 \\
+CatBoost (D) & 1425${}_{-35,+32}$ & 0.228 & 17.0 & 10.0 & 0 & 13.3\% & 5.72 & 0.11 \\
+XGBoost (T+E) & 1415${}_{-33,+36}$ & 0.222 & 17.5 & 14.0 & 0 & 13.1\% & 766.06 & 1.92 \\
+EBM (T+E) & 1383${}_{-33,+31}$ & 0.186 & 19.1 & 12.5 & 0 & 15.7\% & 1109.06 & 0.23 \\
+XGBoost (T) & 1379${}_{-31,+39}$ & 0.181 & 19.2 & 16.8 & 0 & 13.4\% & 766.06 & 0.28 \\
+RealMLP (T) & 1379${}_{-42,+36}$ & 0.201 & 19.3 & 14.8 & 0 & 13.4\% & 5786.69 & 0.25 \\
+ModernNCA (T+E) & 1375${}_{-38,+35}$ & 0.307 & 19.5 & 8.6 & 0 & 12.7\% & 5944.88 & 8.40 \\
+ModernNCA (D) & 1367${}_{-33,+31}$ & 0.183 & 19.8 & 10.0 & 1 & 15.1\% & 14.80 & 0.34 \\
+TorchMLP (T+E) & 1356${}_{-32,+33}$ & 0.199 & 20.3 & 15.0 & 0 & 13.3\% & 2862.05 & 2.16 \\
+FastaiMLP (T+E) & 1356${}_{-35,+32}$ & 0.230 & 20.2 & 11.2 & 0 & 15.7\% & 1358.63 & 8.07 \\
+TabDPT (D) & 1323${}_{-33,+24}$ & 0.258 & 22.0 & 6.1 & 2 & 14.8\% & 27.49 & 8.86 \\
+EBM (T) & 1320${}_{-41,+33}$ & 0.120 & 22.1 & 17.3 & 0 & 16.5\% & 1109.06 & 0.03 \\
+EBM (D) & 1299${}_{-35,+37}$ & 0.153 & 23.0 & 9.7 & 1 & 17.3\% & 5.28 & 0.08 \\
+FastaiMLP (T) & 1269${}_{-40,+38}$ & 0.113 & 24.5 & 19.1 & 0 & 17.2\% & 1358.63 & 0.90 \\
+ExtraTrees (T+E) & 1260${}_{-47,+27}$ & 0.102 & 25.0 & 17.4 & 0 & 18.2\% & 370.85 & 1.47 \\
+TorchMLP (T) & 1238${}_{-33,+33}$ & 0.086 & 25.8 & 22.0 & 0 & 15.9\% & 2862.05 & 0.15 \\
+RealMLP (D) & 1234${}_{-36,+37}$ & 0.083 & 26.1 & 21.8 & 0 & 15.9\% & 36.24 & 0.28 \\
+XGBoost (D) & 1213${}_{-32,+41}$ & 0.032 & 27.1 & 24.8 & 0 & 16.7\% & 2.40 & 0.22 \\
+ExtraTrees (T) & 1200${}_{-32,+35}$ & 0.072 & 27.5 & 22.6 & 0 & 19.4\% & 370.85 & 0.16 \\
+RandomForest (T+E) & 1157${}_{-30,+36}$ & 0.057 & 29.5 & 22.0 & 0 & 19.5\% & 527.42 & 1.39 \\
+LightGBM (D) & 1154${}_{-29,+39}$ & 0.030 & 29.6 & 27.7 & 0 & 17.7\% & 2.90 & 0.13 \\
+RandomForest (T) & 1096${}_{-31,+36}$ & 0.023 & 31.9 & 29.1 & 0 & 20.8\% & 527.42 & 0.12 \\
+TorchMLP (D) & 1082${}_{-34,+36}$ & 0.030 & 32.3 & 28.8 & 0 & 21.4\% & 10.38 & 0.19 \\
+FastaiMLP (D) & 1051${}_{-33,+31}$ & 0.019 & 33.6 & 31.0 & 0 & 23.0\% & 4.73 & 0.62 \\
+Linear (T+E) & 1030${}_{-41,+39}$ & 0.055 & 34.2 & 22.9 & 0 & 28.3\% & 88.63 & 0.26 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.005 & 35.3 & 33.4 & 0 & 25.8\% & 0.45 & 0.07 \\
+Linear (T) & 997${}_{-41,+37}$ & 0.035 & 35.2 & 28.4 & 0 & 29.0\% & 88.63 & 0.09 \\
+Linear (D) & 982${}_{-39,+42}$ & 0.025 & 35.7 & 25.0 & 0 & 30.5\% & 2.27 & 0.11 \\
+ExtraTrees (D) & 956${}_{-51,+44}$ & 0.015 & 36.7 & 32.9 & 0 & 27.5\% & 0.40 & 0.07 \\
+KNN (T+E) & 704${}_{-46,+50}$ & 0.000 & 41.6 & 41.2 & 0 & 48.0\% & 3.71 & 0.17 \\
+KNN (T) & 609${}_{-48,+53}$ & 0.000 & 42.7 & 42.5 & 0 & 50.1\% & 3.71 & 0.04 \\
+KNN (D) & 408${}_{-96,+87}$ & 0.000 & 44.2 & 44.1 & 0 & 59.2\% & 0.07 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/tabpfn-imputed-cls/tabarena_leaderboard.csv b/data/tabpfn-imputed-cls/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9cfacb0044514fd9fcb6b0fd592ac532d061c310
--- /dev/null
+++ b/data/tabpfn-imputed-cls/tabarena_leaderboard.csv
@@ -0,0 +1,46 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
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+TABM (tuned + ensemble),7080.034926002046,2.290320276704609,3436.5218404284533,2.1079046445931953,0.5166525238887076,0.547775172494987,0.09238776741660952,0.04985850997225548,43123.835967101855,114.22499982092596,9.307692307692308,0.17379499999999998,6405.6722231189415,1.880999751885732,2828.4486752645407,1.5957955528425738,0.47136645123500964,0.5540981099375837,0.04617144277378826,0.034316541138176816,44088.87353980956,109.46959390379024,8.0,2,1,1,22,1612.5,34.1,36.5,0.8111888111888111,0.22027167779430223
+TABICL (default),21.933824618351764,2.7954679915028757,10.278805574502684,2.035731958506663,0.45732829959408833,0.5181742657898207,0.07108757868512468,0.03468928052253071,145.15967621212897,119.47187491598106,9.48076923076923,0.17207,19.54939921696981,2.1857474181387158,8.891901835466445,1.7433667301105085,0.46959481282562443,0.583210155619012,0.03487584587193143,0.02062556592021359,120.41628593220491,98.62751622705991,8.0,4,3,1,18,1607.5,35.1,33.8,0.8072552447552448,0.3008875223205248
+TABPFNV2 (tuned),14614.639715819583,4.849548537201351,3877.419600731425,2.254088269272259,0.4562783530638654,0.48513499648162556,0.08179742986099706,0.05007438478688923,72438.91792383758,165.31166707386078,10.326923076923077,0.1868,8173.816975453165,0.8871031337314181,3445.602606086448,0.9952991538273057,0.34109768962419973,0.46901679172287375,0.05804204088839793,0.02327176321840322,43032.16406688419,48.819771718398094,6.0,1,8,1,16,1588.5,31.1,41.1,0.7880244755244755,0.26951399319966235
+REALMLP (tuned + ensemble),17809.543566583365,5.343982117807764,5995.031437071161,5.920653730744878,0.5983187605703805,0.5877560111308218,0.10395608954668528,0.05250154829922825,112219.73072981052,290.95193637081104,10.865384615384615,0.17424,10344.939689996507,4.694504770967695,5786.691668992368,4.267078452745884,0.5696681015084324,0.5635752491354304,0.051850568481074444,0.04632297855309826,87871.6182808383,267.1363813998378,9.0,0,0,3,23,1572.9,29.0,36.1,0.7757867132867133,0.1417961559305308
+AutoGluon 1.3 (4h),5931.009838958377,3.1656774007357082,3673.8246676849526,3.0250106370782723,0.5599854226187498,0.5522297755522069,0.09461405000435809,0.05278876561410328,39712.28933278582,151.42916243848958,11.884615384615385,0.17691,5020.2655313505065,2.664059625731574,2309.213100138395,2.5543334551748154,0.5600808158121042,0.565709489217623,0.04762778419021996,0.04116173129591805,35357.34871646478,128.98489015193604,8.5,1,2,0,23,1544.1,33.4,44.0,0.7526223776223776,0.17858871441651328
+TABPFNV2 (default),11.884418644151115,1.1739113624279316,5.514520630749067,0.6490305586450037,0.5013642769155608,0.5669313754038009,0.09512311589648129,0.06286359741165812,76.29505964660768,41.758850754001486,12.038461538461538,0.1886,8.093020015292698,0.44081105126274955,4.062007578219401,0.4356807523460313,0.3771347187714357,0.5238664820190138,0.05018180969629027,0.021765272352146064,58.62232186481185,25.39697585954705,5.0,4,1,4,17,1541.6,37.1,37.8,0.7491258741258742,0.2935579164181083
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+RF (default),0.9640304686676744,0.07426402477117686,0.5084719578868067,0.0844146006139,0.9951894125278877,0.9701912114787967,0.25755513106395067,0.2479832267773854,6.6754175478812146,4.153857105918795,35.28846153846154,0.213105,0.8910642200046115,0.05825435982810126,0.447836563000179,0.06594795127086661,1.0,1.0,0.17586669886220047,0.11692775501298655,5.654732996519019,3.502947097147378,37.5,0,0,0,26,1000.0,0.0,0.0,0.22071678321678323,0.029920050824686344
+LR (default),4.707631549162742,0.1483236100938585,2.804518891637732,0.1437661673750307,0.9745259999639975,0.9520629989740693,0.305476365274656,0.29332097764816845,36.73103788870433,7.308777223189359,35.67307692307692,0.22103499999999998,4.746849238872528,0.08430976470311483,2.2657084486770183,0.10642460584640503,1.0,1.0,0.22930816077470617,0.22370793213879017,31.50980214431158,4.703818974402271,39.5,0,0,1,25,982.3,41.6,38.5,0.21197552447552448,0.03995938942367327
+XT (default),0.8462119445841536,0.0803421718442542,0.47456795510527094,0.08795126020679146,0.9851460583683391,0.9642266228854909,0.2753142737967973,0.27671727525790224,6.0304600748852675,4.406716139948246,36.65384615384615,0.215405,0.7635703219307794,0.06673479080200195,0.40395335630596185,0.07007418015238884,1.0,1.0,0.18424442460299273,0.1562338348801046,5.148113375558061,3.7424721126192138,39.0,0,0,0,26,955.5,43.9,50.8,0.1896853146853147,0.030378669430214802
+KNN (tuned + ensemble),16.440937072497146,0.2739423143558013,7.036183733958932,0.19964653999651732,1.0,0.9945521006475282,0.47993514547383553,0.5914832997832359,57.836601086323085,11.285407568076662,41.57692307692308,0.31118999999999997,6.799899099932777,0.1030390567249722,3.707274221912526,0.16730320161416595,1.0,1.0,0.4552264030449861,0.6183873456591064,55.06974575171796,8.266636963631449,43.0,0,0,0,26,703.5,49.4,45.5,0.0777972027972028,0.024279443329967162
+KNN (tuned),16.440937072497146,0.06075953357240074,7.036183733958932,0.04304950843110991,1.0,0.9966557577121117,0.5010874288021712,0.6455640735444151,57.836601086323085,2.5027008595459077,42.69230769230769,0.31467999999999996,6.799899099932777,0.036990099483066134,3.707274221912526,0.039402452723266784,1.0,1.0,0.4955223463378593,0.6729856958219811,55.06974575171796,2.091430487051362,44.0,0,0,0,26,609.2,52.7,48.0,0.05244755244755245,0.023502270498812147
+KNN (default),0.2899092884145231,0.0317453768518236,0.13426353967394633,0.031065835648244968,1.0,1.0,0.5923454279012129,0.9491257378249622,1.00065410372272,1.4527699655062962,44.25,0.34582,0.1258228341738383,0.02117468251122369,0.07457399441809831,0.021006283652748647,1.0,1.0,0.6201400300158697,1.0,1.0,1.0057335151210618,45.0,0,0,0,26,407.8,86.4,95.3,0.017045454545454544,0.022663376691509682
diff --git a/data/tabpfn-imputed-cls/tuning-impact-elo-horizontal.pdf b/data/tabpfn-imputed-cls/tuning-impact-elo-horizontal.pdf
new file mode 100644
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new file mode 100644
index 0000000000000000000000000000000000000000..24481787fa054a7bc9a1fd8d8bbf394bb80459fd
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+++ b/data/tabpfn-imputed-cls/tuning-impact-elo-horizontal.png.zip
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+size 153061
diff --git a/data/tabpfn-imputed-reg/figures/critical-diagram.pdf b/data/tabpfn-imputed-reg/figures/critical-diagram.pdf
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diff --git a/data/tabpfn-imputed-reg/figures/critical-diagram.png.zip b/data/tabpfn-imputed-reg/figures/critical-diagram.png.zip
new file mode 100644
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+version https://git-lfs.github.com/spec/v1
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diff --git a/data/tabpfn-imputed-reg/leaderboard.tex b/data/tabpfn-imputed-reg/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..6afd113d8bc993d3ee92cb44469340102255c379
--- /dev/null
+++ b/data/tabpfn-imputed-reg/leaderboard.tex
@@ -0,0 +1,53 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+RealMLP (T+E) & \textcolor{gold}{\textbf{1795${}_{-87,+77}$}} & \textcolor{silver}{\textbf{0.758}} & \textcolor{gold}{\textbf{5.7}} & 3.9 & 0 & \textcolor{silver}{\textbf{1.7\%}} & 7793.93 & 5.22 \\
+TabPFNv2 (T+E) & \textcolor{silver}{\textbf{1781${}_{-93,+99}$}} & \textcolor{silver}{\textbf{0.758}} & \textcolor{silver}{\textbf{5.9}} & \textcolor{gold}{\textbf{1.7}} & \textcolor{gold}{\textbf{3}} & \textcolor{gold}{\textbf{0.8\%}} & 6577.99 & 56.80 \\
+TabDPT (D) & \textcolor{bronze}{\textbf{1766${}_{-86,+85}$}} & \textcolor{gold}{\textbf{0.787}} & 6.4 & \textcolor{silver}{\textbf{2.0}} & \textcolor{gold}{\textbf{3}} & 3.0\% & 35.70 & 46.71 \\
+AutoGluon 1.3 (4h) & 1760${}_{-85,+91}$ & 0.691 & \textcolor{bronze}{\textbf{6.3}} & \textcolor{bronze}{\textbf{3.1}} & \textcolor{bronze}{\textbf{1}} & 3.3\% & 4737.61 & 4.70 \\
+CatBoost (T+E) & 1699${}_{-74,+85}$ & 0.605 & 7.9 & 6.9 & 0 & 4.2\% & 3558.14 & 0.97 \\
+CatBoost (T) & 1663${}_{-83,+73}$ & 0.572 & 9.0 & 7.3 & 0 & 4.3\% & 3558.14 & 0.10 \\
+LightGBM (T+E) & 1622${}_{-82,+67}$ & 0.515 & 10.3 & 8.0 & 0 & 5.5\% & 1003.11 & 4.89 \\
+TabM (T+E) & 1561${}_{-85,+66}$ & 0.487 & 12.1 & 7.1 & 0 & 3.0\% & 4158.29 & 1.95 \\
+TabPFNv2 (T) & 1556${}_{-75,+84}$ & 0.524 & 12.3 & 4.6 & 0 & \textcolor{bronze}{\textbf{2.9\%}} & 6577.99 & 0.50 \\
+XGBoost (T+E) & 1556${}_{-77,+66}$ & 0.453 & 12.6 & 12.0 & 0 & 5.7\% & 933.42 & 2.76 \\
+ModernNCA (T+E) & 1546${}_{-87,+54}$ & 0.371 & 12.9 & 8.2 & 0 & 6.4\% & 9308.22 & 7.89 \\
+XGBoost (T) & 1514${}_{-64,+74}$ & 0.407 & 13.9 & 13.4 & 0 & 5.9\% & 933.42 & 0.39 \\
+LightGBM (T) & 1473${}_{-78,+69}$ & 0.381 & 15.4 & 14.1 & 0 & 6.3\% & 1003.11 & 0.89 \\
+CatBoost (D) & 1467${}_{-78,+81}$ & 0.376 & 15.7 & 12.5 & 0 & 6.8\% & 10.91 & 0.20 \\
+TabPFNv2 (D) & 1427${}_{-79,+91}$ & 0.424 & 17.3 & 6.4 & 0 & 5.5\% & 9.10 & 0.85 \\
+TabM (T) & 1395${}_{-88,+79}$ & 0.361 & 18.6 & 14.8 & 0 & 4.2\% & 4158.29 & 0.21 \\
+RealMLP (T) & 1381${}_{-92,+74}$ & 0.295 & 18.9 & 14.9 & 0 & 4.3\% & 7793.93 & 0.36 \\
+ExtraTrees (T+E) & 1375${}_{-81,+73}$ & 0.241 & 19.1 & 10.9 & 0 & 11.2\% & 623.85 & 1.14 \\
+ModernNCA (T) & 1358${}_{-61,+62}$ & 0.125 & 20.0 & 17.7 & 0 & 8.7\% & 9308.22 & 0.42 \\
+ExtraTrees (T) & 1356${}_{-68,+63}$ & 0.210 & 20.0 & 13.5 & 0 & 11.3\% & 623.85 & 0.29 \\
+TabM (D) & 1336${}_{-73,+68}$ & 0.319 & 20.9 & 15.3 & 0 & 6.0\% & 15.01 & 0.15 \\
+RealMLP (D) & 1328${}_{-76,+59}$ & 0.157 & 21.1 & 17.0 & 0 & 6.6\% & 41.54 & 0.31 \\
+ModernNCA (D) & 1283${}_{-85,+72}$ & 0.061 & 23.1 & 21.8 & 0 & 10.3\% & 27.82 & 0.30 \\
+LightGBM (D) & 1262${}_{-82,+56}$ & 0.016 & 23.9 & 23.6 & 0 & 9.2\% & 4.57 & 0.27 \\
+TorchMLP (T+E) & 1262${}_{-85,+64}$ & 0.136 & 23.9 & 21.6 & 0 & 8.9\% & 5692.02 & 1.35 \\
+RandomForest (T+E) & 1248${}_{-93,+55}$ & 0.122 & 24.7 & 21.3 & 0 & 12.8\% & 633.57 & 1.51 \\
+TorchMLP (T) & 1242${}_{-87,+63}$ & 0.139 & 24.7 & 21.4 & 0 & 9.4\% & 5692.02 & 0.13 \\
+EBM (T+E) & 1215${}_{-71,+90}$ & 0.186 & 25.4 & 9.7 & 0 & 13.1\% & 1904.36 & 0.16 \\
+ExtraTrees (D) & 1192${}_{-71,+66}$ & 0.085 & 26.6 & 23.4 & 0 & 13.7\% & 0.47 & 0.12 \\
+RandomForest (T) & 1186${}_{-70,+71}$ & 0.087 & 26.7 & 24.1 & 0 & 13.3\% & 633.57 & 0.25 \\
+EBM (T) & 1175${}_{-77,+75}$ & 0.151 & 27.0 & 13.0 & 0 & 13.7\% & 1904.36 & 0.02 \\
+XGBoost (D) & 1160${}_{-93,+68}$ & 0.000 & 27.7 & 27.2 & 0 & 10.4\% & 3.79 & 0.31 \\
+EBM (D) & 1102${}_{-84,+86}$ & 0.074 & 29.9 & 27.3 & 0 & 14.7\% & 7.85 & 0.07 \\
+FastaiMLP (T+E) & 1093${}_{-80,+75}$ & 0.000 & 30.0 & 29.5 & 0 & 12.7\% & 2248.07 & 7.53 \\
+FastaiMLP (T) & 1021${}_{-82,+74}$ & 0.000 & 32.1 & 31.6 & 0 & 13.2\% & 2248.07 & 0.89 \\
+TorchMLP (D) & 1008${}_{-73,+72}$ & 0.010 & 32.6 & 31.2 & 0 & 12.8\% & 24.31 & 0.13 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.000 & 32.8 & 31.7 & 0 & 15.3\% & 0.53 & 0.12 \\
+TabICL (D) & 998${}_{-73,+75}$ & 0.000 & 32.8 & 31.7 & 0 & 15.3\% & 0.53 & 0.12 \\
+FastaiMLP (D) & 886${}_{-59,+69}$ & 0.000 & 35.4 & 35.0 & 0 & 18.7\% & 7.09 & 0.84 \\
+Linear (T+E) & 463${}_{-133,+126}$ & 0.000 & 41.1 & 41.1 & 0 & 34.7\% & 149.75 & 0.13 \\
+KNN (T+E) & 419${}_{-130,+87}$ & 0.000 & 41.4 & 41.3 & 0 & 38.4\% & 2.43 & 0.18 \\
+Linear (T) & 416${}_{-140,+114}$ & 0.000 & 41.7 & 41.6 & 0 & 34.8\% & 149.75 & 0.06 \\
+KNN (T) & 342${}_{-156,+115}$ & 0.000 & 42.3 & 42.2 & 0 & 39.1\% & 2.43 & 0.03 \\
+Linear (D) & 310${}_{-151,+116}$ & 0.000 & 42.4 & 42.4 & 0 & 36.2\% & 3.45 & 0.09 \\
+KNN (D) & -34${}_{-190,+194}$ & 0.000 & 44.6 & 44.5 & 0 & 43.3\% & 0.04 & 0.03 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/tabpfn-imputed-reg/tabarena_leaderboard.csv b/data/tabpfn-imputed-reg/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..64547247bebdfaf2dc9bbc5f274bdcdfd255bd3f
--- /dev/null
+++ b/data/tabpfn-imputed-reg/tabarena_leaderboard.csv
@@ -0,0 +1,46 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
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+TABPFNV2 (tuned + ensemble),13720.707571309333,144.4128799729877,6347.594191591684,104.41432658354127,0.2415939115786189,0.3302133765170469,0.008040709459012918,0.018722358351318587,189200.55764735988,8406.36796869519,5.857142857142857,4.11819,6554.713698530197,35.598623005549115,6577.9935429270845,56.7950324715632,0.06610997682360657,0.25030247372883757,0.0006200613284135237,0.005184858759349278,150467.84733998418,5370.844516439232,2.0,3,2,0,2,1781.4,98.1,92.6,0.8896103896103896,0.5884711779448623
+AutoGluon 1.3 (4h),6519.395389483089,8.374968978715321,4847.228712217428,6.324376295240136,0.3085910704411772,0.3756961336550062,0.033251519267014036,0.049602203108293184,112121.1836885888,503.3434027862167,6.285714285714286,4.16484,5893.662480862936,2.3565427462259927,4737.606472356081,4.698652813671368,0.23133371369265834,0.30893679782746253,0.015883109174547605,0.03516384582661509,94686.22259252104,291.4795027754577,5.0,1,1,0,5,1760.0,90.7,84.6,0.8798701298701299,0.3248917748917749
+TABDPT (default),60.7062628246489,23.490844258051073,38.96865681756451,39.22338625261095,0.21328360616453232,0.30831524812970973,0.029680920262059787,0.014635575677645899,964.3036575691822,2256.3060022287436,6.428571428571429,4.26747,45.33390234311422,23.403914364178977,35.69555633627602,46.714399928500946,0.18149452492987356,0.2944607543670049,0.0039773225368111564,0.00227534979039058,904.9754515353668,2312.870048476579,4.0,3,0,0,4,1765.5,84.3,85.5,0.8766233766233766,0.49988662131519274
+CAT (tuned + ensemble),11360.685129829437,1.178349671288142,7479.02149818403,1.2406086528694698,0.3954429862751284,0.45630330723246004,0.041752236791189334,0.05160245061199872,183467.75682165983,88.84726445881574,7.857142857142857,4.20863,12951.390986363092,0.6367310682932535,3558.1422644149734,0.9657383741190037,0.36849553490903875,0.48332107057126966,0.01735502485245788,0.056013107516843214,172301.38506843304,80.04598787510639,8.0,0,0,0,7,1698.8,84.7,73.2,0.8441558441558441,0.1439255189255189
+CAT (tuned),11360.685129829437,0.18431518115694562,7479.02149818403,0.19033233887232168,0.42772839942623186,0.4797279640538269,0.04293561079285979,0.05673883273885333,183467.75682165983,14.175855537943391,9.0,4.23064,12951.390986363092,0.0923632542292277,3558.1422644149734,0.10450043094654878,0.5120020111687084,0.49061208598211514,0.018380899773098314,0.05607775104310802,172301.38506843304,9.480941702142749,10.0,0,0,1,6,1663.2,72.8,82.3,0.8181818181818182,0.13734955520669806
+GBM (tuned + ensemble),1550.5350551957176,11.328482860232157,1095.0718838551823,8.219062398413474,0.48543215706848314,0.5340835874571865,0.054708320326180546,0.06759479279110976,25605.476583745745,681.2643137899079,10.285714285714286,4.21165,1305.722465435664,4.427071142196655,1003.1068347227413,4.891030532077303,0.3027896779514977,0.44773308883751844,0.02669651687138408,0.03428335611022812,24184.993298853384,538.7114397419949,7.0,0,0,0,7,1622.5,66.9,81.8,0.788961038961039,0.12504095737178442
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+GBM (tuned),1550.5350551957176,1.4077377538832407,1095.0718838551823,1.2781273508842104,0.6186778290078211,0.6418712970322261,0.06336854992568382,0.08812536702511353,25605.476583745745,102.58551273372913,15.428571428571429,4.23482,1305.722465435664,0.5831021388371785,1003.1068347227413,0.8935510846842087,0.5552965190223861,0.5497771953690628,0.027972044011269404,0.0771469045019801,24184.993298853384,73.3040824604662,14.0,0,0,0,7,1473.4,68.1,77.4,0.672077922077922,0.07088017024991815
+CAT (default),34.406632555855644,0.13612949961707704,24.995110587630016,0.18681141105942162,0.6237900244125646,0.6675667972832624,0.06846154890412891,0.08126436753648981,594.4248060555409,11.797969177620454,15.714285714285714,4.21395,45.3870238383611,0.10361001491546631,10.909956325719385,0.1965340299278453,0.5847419575275037,0.6867027709758126,0.022724551212545796,0.08888361196746222,412.72250349454265,9.618489444383828,16.0,0,0,0,7,1467.3,80.1,77.3,0.6655844155844156,0.07979304309136241
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+XT (tuned + ensemble),660.5781686419533,1.1831508216403779,587.7119735330767,1.316998538867821,0.7590185126625372,0.7873727562992079,0.11152236742279202,0.09736117781705758,12611.439486121615,82.99538684812504,19.142857142857142,5.04794,628.4651173035304,0.7619378328323364,623.8510097889447,1.1438343022436304,1.0,0.9703763858301026,0.025903530399211117,0.10013551316301275,14081.344732130377,68.20231667792302,25.0,0,0,0,7,1375.1,72.4,80.6,0.5876623376623377,0.09159277504105091
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+NN_TORCH (tuned + ensemble),12800.476561368458,1.1380399908338272,7081.227309206272,1.497767305174234,0.8644212313341298,0.8516898463073783,0.08862343937037091,0.15385577166637224,182552.53268590602,90.9320692691531,23.857142857142858,4.65351,7304.863066792488,0.6995723327000936,5692.02320138285,1.353764106339938,1.0,0.8805586694349655,0.0931488267963303,0.09843675731093122,158151.6866749578,90.00555655937438,23.0,0,0,0,7,1262.2,63.2,84.2,0.4805194805194805,0.046321552208279895
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+NN_TORCH (tuned),12800.476561368458,0.08545414417509048,7081.227309206272,0.11421111903323287,0.8608314951688506,0.858766925529132,0.09394846170102965,0.1725237564760349,182552.53268590602,7.06193729144408,24.714285714285715,4.7148,7304.863066792488,0.06568752924601237,5692.02320138285,0.131112832826372,1.0,0.9430447630912011,0.09751345914708842,0.10970665592293476,158151.6866749578,6.1490644891314155,24.0,0,0,0,7,1242.5,62.3,86.5,0.461038961038961,0.04663841801689928
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+NN_TORCH (default),45.35887446289971,0.0802082171515813,31.67622236459062,0.1165329153506545,0.9903234435493359,0.967098701481312,0.12780665628037344,0.23160692294055926,751.5426969414585,7.0142840459527545,32.57142857142857,4.81722,35.183143361409506,0.06712730725606282,24.305650569576496,0.13416367617779604,1.0,0.9880227178998348,0.14511201063493562,0.13614151845721162,546.0088939544379,6.398331846186531,33.0,0,0,0,7,1007.6,71.2,72.7,0.2824675324675325,0.03209949096583058
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+LR (tuned + ensemble),152.34852822886572,0.09327742031642369,140.44301462951617,0.1463023584921992,1.0,1.0,0.34749111122086884,0.6284394248904203,2977.405199523172,9.309183761718689,41.142857142857146,8.15038,150.0536302089691,0.057510574658711754,149.75412196503902,0.1287132100674069,1.0,1.0,0.26203835697566735,0.7414458856888512,3344.1604489764304,7.22988242769623,41.0,0,0,0,7,462.9,125.3,132.3,0.08766233766233766,0.024356949123772592
+KNN (tuned + ensemble),4.245547793025062,0.1591204832470606,2.4746151721359886,0.1895989937949328,1.0,0.991196682740939,0.3842177651885135,0.7048115612733296,62.40052816223211,11.931188445938428,41.42857142857143,8.41865,2.490966773033142,0.10710635185241699,2.4322953132177623,0.17676389939136214,1.0,1.0,0.41352429770943944,0.7907767115559304,56.15178324522979,13.124172379663548,42.0,0,0,0,7,419.0,86.6,129.4,0.08116883116883117,0.024194887702493926
+LR (tuned),152.34852822886572,0.037903934433346706,140.44301462951617,0.06382438080033122,1.0,1.0,0.34766987292821344,0.6285783845355511,2977.405199523172,3.6512091758703176,41.714285714285715,8.15927,150.0536302089691,0.03235630989074707,149.75412196503902,0.06193413547254449,1.0,1.0,0.2620998903501569,0.7430806606996537,3344.1604489764304,4.067640041721783,42.0,0,0,0,7,415.6,113.1,139.1,0.07467532467532467,0.024046771149923245
+KNN (tuned),4.245547793025062,0.02863398733593169,2.4746151721359886,0.030004193594300673,1.0,0.997572722674734,0.3910594237799242,0.7476794241583206,62.40052816223211,1.9760324688350897,42.285714285714285,8.54422,2.490966773033142,0.014579184850056966,2.4322953132177623,0.02910016936139115,1.0,1.0,0.420237807146593,0.813866558903926,56.15178324522979,1.7112793648526077,43.0,0,0,0,7,341.8,115.0,155.3,0.06168831168831169,0.023694163654458606
+LR (default),3.9008307816490295,0.0549199554655287,3.239606778702032,0.07872063839931824,1.0,1.0,0.3619225843912718,0.6950518956016436,71.92078534572094,4.852563458042672,42.42857142857143,8.22758,3.3858322699864707,0.05012191931406657,3.447264865169665,0.08708413534655632,1.0,1.0,0.26184876537499124,0.7556433033178954,75.37476586504175,4.100500531123858,42.0,0,0,0,7,310.2,115.6,150.4,0.05844155844155844,0.023594631651110056
+KNN (default),0.06228995323181152,0.01962243337479849,0.04219198036913814,0.02726788157828385,1.0,0.9997271492012134,0.432705772179096,0.9384821422991884,1.0,1.6381446334470857,44.57142857142857,9.55646,0.04463104406992594,0.014348586400349935,0.043660770227879654,0.02863989301467053,1.0,1.0,0.4573857800165779,1.0,1.0,1.5792045889101338,45.0,0,0,0,7,-34.1,193.4,189.9,0.00974025974025974,0.022448979591836733
diff --git a/data/tabpfn-imputed-reg/tuning-impact-elo-horizontal.pdf b/data/tabpfn-imputed-reg/tuning-impact-elo-horizontal.pdf
new file mode 100644
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Binary files /dev/null and b/data/tabpfn-imputed-reg/tuning-impact-elo-horizontal.pdf differ
diff --git a/data/tabpfn-imputed-reg/tuning-impact-elo-horizontal.png.zip b/data/tabpfn-imputed-reg/tuning-impact-elo-horizontal.png.zip
new file mode 100644
index 0000000000000000000000000000000000000000..fd6e402fb997d49c8c621af728c7533dc827169a
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+++ b/data/tabpfn-imputed-reg/tuning-impact-elo-horizontal.png.zip
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 151423
diff --git a/data/tabpfn-imputed/figures/critical-diagram.pdf b/data/tabpfn-imputed/figures/critical-diagram.pdf
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diff --git a/data/tabpfn-imputed/figures/critical-diagram.png.zip b/data/tabpfn-imputed/figures/critical-diagram.png.zip
new file mode 100644
index 0000000000000000000000000000000000000000..2730348b3362dad5e4aa2ad742a14121f30a86a1
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+++ b/data/tabpfn-imputed/figures/critical-diagram.png.zip
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+version https://git-lfs.github.com/spec/v1
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+size 321507
diff --git a/data/tabpfn-imputed/leaderboard.tex b/data/tabpfn-imputed/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..99c020662172dc8f3d1348c07a88398fb7596cd3
--- /dev/null
+++ b/data/tabpfn-imputed/leaderboard.tex
@@ -0,0 +1,53 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+TabPFNv2 (T+E) & \textcolor{gold}{\textbf{1714${}_{-31,+44}$}} & \textcolor{gold}{\textbf{0.712}} & \textcolor{gold}{\textbf{5.8}} & \textcolor{gold}{\textbf{2.0}} & \textcolor{gold}{\textbf{11}} & \textcolor{gold}{\textbf{3.8\%}} & 3899.42 & 55.83 \\
+RealMLP (T+E) & \textcolor{silver}{\textbf{1582${}_{-30,+31}$}} & 0.477 & \textcolor{silver}{\textbf{9.8}} & 6.0 & 0 & 8.6\% & 6131.85 & 4.53 \\
+TabM (T+E) & \textcolor{bronze}{\textbf{1578${}_{-36,+26}$}} & 0.484 & \textcolor{bronze}{\textbf{9.9}} & 4.9 & 2 & \textcolor{bronze}{\textbf{7.9\%}} & 3372.56 & 1.66 \\
+AutoGluon 1.3 (4h) & 1557${}_{-35,+33}$ & \textcolor{bronze}{\textbf{0.493}} & 10.7 & 4.8 & 2 & 8.2\% & 2727.51 & 3.24 \\
+TabPFNv2 (T) & 1556${}_{-32,+34}$ & \textcolor{silver}{\textbf{0.540}} & 10.7 & \textcolor{bronze}{\textbf{3.9}} & 1 & \textcolor{silver}{\textbf{7.1\%}} & 3899.42 & 0.98 \\
+LightGBM (T+E) & 1518${}_{-37,+27}$ & 0.361 & 12.3 & 7.3 & 1 & 10.4\% & 771.57 & 2.49 \\
+TabPFNv2 (D) & 1498${}_{-41,+31}$ & 0.483 & 13.2 & \textcolor{silver}{\textbf{3.8}} & \textcolor{bronze}{\textbf{4}} & 8.7\% & 4.22 & 0.55 \\
+CatBoost (T+E) & 1477${}_{-33,+30}$ & 0.342 & 14.1 & 10.1 & 0 & 10.1\% & 2034.85 & 0.80 \\
+TabICL (D) & 1470${}_{-33,+28}$ & 0.428 & 14.4 & 4.1 & \textcolor{bronze}{\textbf{4}} & 8.9\% & 7.27 & 1.64 \\
+TabM (T) & 1469${}_{-26,+26}$ & 0.382 & 14.5 & 8.4 & 0 & 9.1\% & 3372.56 & 0.21 \\
+CatBoost (T) & 1458${}_{-26,+29}$ & 0.316 & 14.9 & 9.6 & 0 & 10.3\% & 2034.85 & 0.10 \\
+LightGBM (T) & 1448${}_{-29,+28}$ & 0.288 & 15.3 & 13.5 & 0 & 10.9\% & 771.57 & 0.32 \\
+XGBoost (T+E) & 1425${}_{-29,+26}$ & 0.271 & 16.5 & 13.5 & 0 & 11.5\% & 828.74 & 2.31 \\
+CatBoost (D) & 1415${}_{-32,+36}$ & 0.259 & 16.7 & 10.4 & 0 & 11.9\% & 8.51 & 0.12 \\
+ModernNCA (T) & 1397${}_{-30,+34}$ & 0.228 & 17.7 & 9.8 & 1 & 11.0\% & 6147.69 & 0.48 \\
+TabM (D) & 1396${}_{-30,+30}$ & 0.295 & 17.8 & 11.9 & 0 & 12.1\% & 12.24 & 0.15 \\
+XGBoost (T) & 1388${}_{-25,+23}$ & 0.229 & 18.1 & 15.9 & 0 & 11.8\% & 828.74 & 0.34 \\
+ModernNCA (T+E) & 1388${}_{-28,+31}$ & 0.320 & 18.1 & 8.5 & 0 & 11.4\% & 6147.69 & 8.15 \\
+TabDPT (D) & 1374${}_{-28,+31}$ & 0.370 & 18.7 & 4.3 & \textcolor{silver}{\textbf{5}} & 12.3\% & 28.84 & 9.01 \\
+RealMLP (T) & 1363${}_{-29,+29}$ & 0.221 & 19.2 & 14.9 & 0 & 11.5\% & 6131.85 & 0.26 \\
+EBM (T+E) & 1341${}_{-32,+40}$ & 0.186 & 20.4 & 11.8 & 0 & 15.2\% & 1331.68 & 0.21 \\
+ModernNCA (D) & 1338${}_{-32,+31}$ & 0.157 & 20.5 & 11.3 & 1 & 14.1\% & 16.16 & 0.31 \\
+TorchMLP (T+E) & 1328${}_{-33,+24}$ & 0.186 & 21.0 & 16.0 & 0 & 12.4\% & 3704.30 & 2.07 \\
+FastaiMLP (T+E) & 1301${}_{-33,+30}$ & 0.181 & 22.3 & 12.9 & 0 & 15.1\% & 1459.62 & 8.06 \\
+EBM (T) & 1283${}_{-32,+34}$ & 0.127 & 23.2 & 16.2 & 0 & 15.9\% & 1331.68 & 0.02 \\
+ExtraTrees (T+E) & 1269${}_{-27,+28}$ & 0.131 & 23.8 & 15.5 & 0 & 16.7\% & 416.39 & 1.39 \\
+EBM (D) & 1258${}_{-34,+25}$ & 0.136 & 24.5 & 11.2 & 1 & 16.7\% & 5.89 & 0.07 \\
+RealMLP (D) & 1246${}_{-32,+22}$ & 0.099 & 25.0 & 20.6 & 0 & 14.0\% & 37.06 & 0.31 \\
+TorchMLP (T) & 1232${}_{-30,+35}$ & 0.097 & 25.6 & 21.9 & 0 & 14.5\% & 3704.30 & 0.14 \\
+ExtraTrees (T) & 1226${}_{-38,+29}$ & 0.102 & 25.9 & 19.8 & 0 & 17.6\% & 416.39 & 0.18 \\
+FastaiMLP (T) & 1221${}_{-37,+31}$ & 0.089 & 26.1 & 20.9 & 0 & 16.4\% & 1459.62 & 0.89 \\
+XGBoost (D) & 1195${}_{-26,+33}$ & 0.025 & 27.2 & 25.3 & 0 & 15.4\% & 3.05 & 0.24 \\
+LightGBM (D) & 1170${}_{-29,+28}$ & 0.027 & 28.4 & 26.7 & 0 & 15.9\% & 3.39 & 0.16 \\
+RandomForest (T+E) & 1167${}_{-29,+30}$ & 0.071 & 28.5 & 21.8 & 0 & 18.1\% & 572.67 & 1.42 \\
+RandomForest (T) & 1109${}_{-29,+37}$ & 0.036 & 30.8 & 27.9 & 0 & 19.2\% & 572.67 & 0.14 \\
+TorchMLP (D) & 1070${}_{-28,+40}$ & 0.026 & 32.4 & 29.2 & 0 & 19.6\% & 11.82 & 0.15 \\
+FastaiMLP (D) & 1023${}_{-32,+31}$ & 0.015 & 34.0 & 31.8 & 0 & 22.1\% & 5.18 & 0.65 \\
+ExtraTrees (D) & 1007${}_{-29,+35}$ & 0.030 & 34.5 & 30.3 & 0 & 24.6\% & 0.42 & 0.08 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.004 & 34.8 & 33.0 & 0 & 23.5\% & 0.47 & 0.07 \\
+Linear (T+E) & 972${}_{-30,+27}$ & 0.044 & 35.7 & 25.3 & 0 & 29.7\% & 97.00 & 0.22 \\
+Linear (T) & 942${}_{-35,+38}$ & 0.028 & 36.6 & 30.5 & 0 & 30.2\% & 97.00 & 0.09 \\
+Linear (D) & 922${}_{-45,+27}$ & 0.020 & 37.1 & 27.4 & 0 & 31.7\% & 2.99 & 0.10 \\
+KNN (T+E) & 691${}_{-49,+42}$ & 0.000 & 41.5 & 41.2 & 0 & 46.0\% & 3.41 & 0.18 \\
+KNN (T) & 607${}_{-51,+41}$ & 0.000 & 42.6 & 42.5 & 0 & 47.8\% & 3.41 & 0.04 \\
+KNN (D) & 371${}_{-104,+63}$ & 0.000 & 44.3 & 44.2 & 0 & 55.8\% & 0.07 & 0.03 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/tabpfn-imputed/tabarena_leaderboard.csv b/data/tabpfn-imputed/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..dedb326e7f24e0ad8d6e15b4e7acee517e8584f7
--- /dev/null
+++ b/data/tabpfn-imputed/tabarena_leaderboard.csv
@@ -0,0 +1,46 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+TABPFNV2 (tuned + ensemble),14425.017745771953,147.33083187377815,4401.396029095723,73.66242671660264,0.28759504252950974,0.3583482480018585,0.03810528355622711,0.026782979752020025,97206.53847125142,5748.512821917254,5.803030303030303,0.22151,6554.713698530197,48.78112569650014,3899.4164285155534,55.8334682198027,0.16748308582785468,0.3535617963732386,0.014027742147710298,0.009805462772927088,76922.9404189982,3038.197671565286,2.0,11,6,1,15,1714.4,43.7,30.9,0.8908402203856749,0.48937326678683374
+REALMLP (tuned + ensemble),17640.05257734851,4.903230364073808,6337.298676302214,5.714285721500293,0.5227695654892056,0.5349735605494492,0.08551898066723945,0.04651514482247435,135567.2437894661,291.2042190402348,9.772727272727273,0.23449,10127.618620618185,4.333946492936876,6131.852125259443,4.526544125425011,0.48215030896347355,0.5386632171507916,0.04086003335580146,0.02997010750546727,109144.7404232934,273.1739755067715,8.0,0,1,4,28,1582.4,30.5,30.0,0.8006198347107438,0.165506062248297
+TABM (tuned + ensemble),7759.403633462299,2.1001035646156025,4063.8948551919407,2.0262676290994976,0.5158505640872573,0.5420484616540082,0.07908714239356904,0.05662146691038834,68994.91604679843,112.78737728828092,9.909090909090908,0.22996,6619.307261109352,1.565106577343411,3372.559747313985,1.6579582265448953,0.47020083976979304,0.5421819053386484,0.038378144006785075,0.03425172068662737,47614.825535817596,107.26839105814781,8.0,2,1,2,28,1578.1,25.9,35.4,0.7975206611570248,0.20327737247639596
+AutoGluon 1.3 (4h),6055.818895130286,4.270678644549565,3922.7285559191146,3.7248760797186677,0.5066593479144164,0.5147832454528006,0.08159775560552752,0.05211282811287083,55071.75177189554,226.07763766376502,10.696969696969697,0.23252,5245.15874774456,2.643711381488376,2727.5131652494383,3.239980099911348,0.4597995521540763,0.5451307928879923,0.03289499571493004,0.03718969914596063,42990.839716832954,133.93997628187762,8.0,2,3,0,28,1557.0,32.7,34.4,0.7796143250688705,0.20962269694156876
+TABPFNV2 (tuned),14425.017745771953,5.209287579613502,4401.396029095723,2.5426283197700723,0.46046523778783577,0.49191924249583513,0.07053987986416366,0.05344001229510615,97206.53847125142,204.9255427422306,10.742424242424242,0.22735,6554.713698530197,0.8280300378799439,3899.4164285155534,0.9826616035428936,0.34176855083335494,0.4964169422314872,0.03437211181910249,0.021856445988952963,76922.9404189982,51.81666634218519,6.0,1,10,1,21,1556.0,33.5,31.8,0.7785812672176309,0.25839590411455043
+GBM (tuned + ensemble),1415.9841766067627,4.799208115006135,910.5198965732433,4.069284327482318,0.6392387450549837,0.6519851738431574,0.10428405536733554,0.06311285033655824,13668.933422648086,267.51058203721857,12.348484848484848,0.22857,1305.722465435664,2.759434594048394,771.5692555555095,2.4904788987789197,0.6693216689945842,0.6727922701071665,0.04583211069062454,0.04469640874832902,13185.517460723013,113.47473834277966,11.0,1,0,2,30,1518.3,26.8,36.5,0.7420798898071626,0.13716214969036036
+TABPFNV2 (default),11.52095359335042,1.0633954972129076,6.06940244179637,0.672221391108446,0.5171946106727229,0.5762036040928518,0.08650644617919911,0.0712395059554867,98.45527177302218,43.21321073170591,13.151515151515152,0.22839,8.920613225301107,0.4424108028411865,4.216795518748306,0.554313340790968,0.3939035196311629,0.49711705993401867,0.046436046436047484,0.024781120664368425,86.18639524525906,32.62493249187355,5.0,4,1,6,22,1497.5,30.1,40.5,0.7238292011019284,0.2643791451026961
+CAT (tuned + ensemble),6111.719856242539,1.006158811716921,3790.183958691414,0.8907615430657749,0.6581825114313259,0.6582788891752136,0.1008693477859659,0.06664337122899275,60543.81785886913,53.55072795645876,14.090909090909092,0.23306,3546.8591198126474,0.7182260619269477,2034.851316438205,0.804742177327474,0.6112565392764306,0.6936836326181555,0.057913957490063894,0.056013107516843214,22945.70625086803,41.43902270876451,13.0,0,0,1,32,1477.0,29.5,32.8,0.7024793388429752,0.09860544117243167
+TABICL (default),17.601916466818917,2.220749478067212,8.223267143306801,1.62619082060654,0.5724404784680696,0.6158184269020356,0.088559472294408,0.06951993310511383,118.07631364935412,95.53326061925603,14.424242424242424,0.21659,12.685983737309774,1.1608605914645724,7.267564825342236,1.640227848921365,0.5810932894452219,0.7216187048161464,0.04180362345559929,0.026535607179780867,110.9966550885031,89.55280278440337,10.0,4,3,1,25,1469.6,27.7,32.9,0.6949035812672176,0.24375608168176008
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+LR (tuned),170.87050911529295,0.11554940750301887,121.02102704404008,0.10905393298119077,0.972064260567699,0.9557824705647567,0.30240282773179056,0.3364007678210794,1772.9723454272128,5.509546013888674,36.59090909090909,0.25255,158.05876021915012,0.058829413519965276,96.99876412252586,0.0862505760249892,1.0,1.0,0.2610498572359117,0.2575122541077485,1402.8748923675635,4.067640041721783,40.5,0,0,0,33,941.9,37.3,34.9,0.19111570247933884,0.0328071081462201
+LR (default),4.536491992417409,0.12851071365754613,2.8968102616210682,0.12996863092563715,0.9799295757292101,0.9622314537371455,0.3174498056933321,0.3785366269110268,44.19552977352603,6.787762182097637,37.10606060606061,0.26248,3.583410120010376,0.07990590731302898,2.994106463982051,0.104980896680783,1.0,1.0,0.26184876537499124,0.29385559917332205,34.79737790723628,4.682238532824547,40.5,0,0,1,32,921.5,27.0,44.9,0.17940771349862258,0.036488077168887134
+KNN (tuned + ensemble),13.854036316245494,0.24958616836303815,6.0685782814510345,0.19751524231739334,1.0,0.9938403453340091,0.459631458746646,0.6155226279781042,58.80470682969773,11.4223913906534,41.54545454545455,0.31971,5.889678266313341,0.10710635185241699,3.411499284183487,0.17676389939136214,1.0,1.0,0.4456786012073394,0.6488391163428452,55.92066201498072,9.83646280007513,43.0,0,0,0,33,690.8,41.6,48.8,0.07851239669421488,0.024261507287775874
+KNN (tuned),13.854036316245494,0.05394502376466488,6.0685782814510345,0.040282320435423104,1.0,0.9968502654314559,0.4777481550095733,0.6672249054928193,58.80470682969773,2.390983322122401,42.60606060606061,0.34215,5.889678266313341,0.029062509536743164,3.411499284183487,0.03624739765555662,1.0,1.0,0.4641563796701971,0.740394718900499,55.92066201498072,2.000488003155834,44.0,0,0,0,33,606.9,40.8,50.1,0.05440771349862259,0.02354297510758564
+KNN (default),0.24162639913334188,0.029173843387000086,0.11473320891232033,0.030260209027344123,1.0,0.9999421225578331,0.5584824706268245,0.9468680054407072,1.0005153544482035,1.4920918647664636,44.31818181818182,0.40639,0.11169254779815674,0.019644896189371746,0.06555315548394994,0.02595407415488982,1.0,1.0,0.5530598349091809,1.0,1.0,1.2186499153225778,45.0,0,0,0,33,371.3,62.9,103.8,0.015495867768595042,0.022617898518851785
diff --git a/data/tabpfn-imputed/tuning-impact-elo-horizontal.pdf b/data/tabpfn-imputed/tuning-impact-elo-horizontal.pdf
new file mode 100644
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diff --git a/data/tabpfn-imputed/tuning-impact-elo-horizontal.png.zip b/data/tabpfn-imputed/tuning-impact-elo-horizontal.png.zip
new file mode 100644
index 0000000000000000000000000000000000000000..f887f59f0106d027450d392018487f91be293dec
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diff --git a/data/tabpfn-reg/figures/critical-diagram.pdf b/data/tabpfn-reg/figures/critical-diagram.pdf
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+version https://git-lfs.github.com/spec/v1
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diff --git a/data/tabpfn-reg/leaderboard.tex b/data/tabpfn-reg/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..a0a60e687d0a6846a95fb9408249a9f4c91c6174
--- /dev/null
+++ b/data/tabpfn-reg/leaderboard.tex
@@ -0,0 +1,52 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+RealMLP (T+E) & \textcolor{gold}{\textbf{1793${}_{-100,+113}$}} & \textcolor{silver}{\textbf{0.758}} & \textcolor{gold}{\textbf{5.7}} & 3.9 & 0 & \textcolor{silver}{\textbf{1.7\%}} & 7793.93 & 5.22 \\
+TabPFNv2 (T+E) & \textcolor{silver}{\textbf{1785${}_{-75,+103}$}} & \textcolor{silver}{\textbf{0.758}} & \textcolor{silver}{\textbf{5.9}} & \textcolor{gold}{\textbf{1.7}} & \textcolor{gold}{\textbf{3}} & \textcolor{gold}{\textbf{0.8\%}} & 6577.99 & 56.80 \\
+AutoGluon 1.3 (4h) & \textcolor{bronze}{\textbf{1768${}_{-74,+85}$}} & 0.691 & \textcolor{bronze}{\textbf{6.3}} & \textcolor{bronze}{\textbf{3.1}} & \textcolor{bronze}{\textbf{1}} & 3.3\% & 4737.61 & 4.70 \\
+TabDPT (D) & 1758${}_{-79,+93}$ & \textcolor{gold}{\textbf{0.787}} & 6.4 & \textcolor{silver}{\textbf{2.0}} & \textcolor{gold}{\textbf{3}} & 3.0\% & 35.70 & 46.71 \\
+CatBoost (T+E) & 1703${}_{-74,+95}$ & 0.605 & 7.9 & 6.9 & 0 & 4.2\% & 3558.14 & 0.97 \\
+CatBoost (T) & 1665${}_{-72,+81}$ & 0.572 & 9.0 & 7.3 & 0 & 4.3\% & 3558.14 & 0.10 \\
+LightGBM (T+E) & 1625${}_{-73,+74}$ & 0.515 & 10.3 & 8.0 & 0 & 5.5\% & 1003.11 & 4.89 \\
+TabM (T+E) & 1558${}_{-84,+92}$ & 0.487 & 12.1 & 7.1 & 0 & 3.0\% & 4158.29 & 1.95 \\
+TabPFNv2 (T) & 1558${}_{-82,+85}$ & 0.524 & 12.1 & 4.6 & 0 & \textcolor{bronze}{\textbf{2.9\%}} & 6577.99 & 0.50 \\
+XGBoost (T+E) & 1548${}_{-80,+67}$ & 0.453 & 12.6 & 12.0 & 0 & 5.7\% & 933.42 & 2.76 \\
+ModernNCA (T+E) & 1540${}_{-68,+80}$ & 0.371 & 12.9 & 8.2 & 0 & 6.4\% & 9308.22 & 7.89 \\
+XGBoost (T) & 1517${}_{-78,+78}$ & 0.407 & 13.9 & 13.4 & 0 & 5.9\% & 933.42 & 0.39 \\
+LightGBM (T) & 1476${}_{-76,+75}$ & 0.381 & 15.4 & 14.1 & 0 & 6.3\% & 1003.11 & 0.89 \\
+CatBoost (D) & 1461${}_{-59,+85}$ & 0.376 & 15.6 & 12.5 & 0 & 6.8\% & 10.91 & 0.20 \\
+TabPFNv2 (D) & 1423${}_{-88,+85}$ & 0.424 & 17.1 & 6.4 & 0 & 5.5\% & 9.10 & 0.85 \\
+TabM (T) & 1390${}_{-76,+80}$ & 0.361 & 18.4 & 14.8 & 0 & 4.2\% & 4158.29 & 0.21 \\
+RealMLP (T) & 1386${}_{-68,+68}$ & 0.295 & 18.7 & 14.9 & 0 & 4.3\% & 7793.93 & 0.36 \\
+ExtraTrees (T+E) & 1369${}_{-57,+97}$ & 0.241 & 19.1 & 10.9 & 0 & 11.2\% & 623.85 & 1.14 \\
+ModernNCA (T) & 1352${}_{-67,+76}$ & 0.125 & 20.0 & 17.7 & 0 & 8.7\% & 9308.22 & 0.42 \\
+ExtraTrees (T) & 1350${}_{-56,+80}$ & 0.210 & 20.0 & 13.5 & 0 & 11.3\% & 623.85 & 0.29 \\
+TabM (D) & 1344${}_{-76,+74}$ & 0.319 & 20.4 & 15.2 & 0 & 6.0\% & 15.01 & 0.15 \\
+RealMLP (D) & 1336${}_{-84,+75}$ & 0.157 & 20.9 & 16.9 & 0 & 6.6\% & 41.54 & 0.31 \\
+ModernNCA (D) & 1281${}_{-70,+78}$ & 0.061 & 23.0 & 21.7 & 0 & 10.3\% & 27.82 & 0.30 \\
+LightGBM (D) & 1264${}_{-80,+70}$ & 0.016 & 23.7 & 23.5 & 0 & 9.2\% & 4.57 & 0.27 \\
+TorchMLP (T+E) & 1260${}_{-68,+85}$ & 0.136 & 23.6 & 21.5 & 0 & 8.9\% & 5692.02 & 1.35 \\
+TorchMLP (T) & 1236${}_{-70,+78}$ & 0.139 & 24.4 & 21.3 & 0 & 9.4\% & 5692.02 & 0.13 \\
+RandomForest (T+E) & 1232${}_{-78,+79}$ & 0.122 & 24.7 & 21.3 & 0 & 12.8\% & 633.57 & 1.51 \\
+EBM (T+E) & 1226${}_{-75,+65}$ & 0.186 & 25.0 & 9.7 & 0 & 13.1\% & 1904.36 & 0.16 \\
+ExtraTrees (D) & 1186${}_{-70,+56}$ & 0.085 & 26.6 & 23.4 & 0 & 13.7\% & 0.47 & 0.12 \\
+EBM (T) & 1180${}_{-68,+85}$ & 0.151 & 26.6 & 12.9 & 0 & 13.7\% & 1904.36 & 0.02 \\
+RandomForest (T) & 1175${}_{-71,+82}$ & 0.087 & 26.7 & 24.1 & 0 & 13.3\% & 633.57 & 0.25 \\
+XGBoost (D) & 1162${}_{-79,+81}$ & 0.000 & 27.4 & 26.9 & 0 & 10.4\% & 3.79 & 0.31 \\
+EBM (D) & 1098${}_{-79,+67}$ & 0.074 & 29.4 & 27.1 & 0 & 14.7\% & 7.85 & 0.07 \\
+FastaiMLP (T+E) & 1090${}_{-71,+71}$ & 0.000 & 29.6 & 29.2 & 0 & 12.7\% & 2248.07 & 7.53 \\
+FastaiMLP (T) & 1026${}_{-76,+71}$ & 0.000 & 31.7 & 31.3 & 0 & 13.2\% & 2248.07 & 0.89 \\
+TorchMLP (D) & 1003${}_{-79,+82}$ & 0.010 & 32.3 & 31.0 & 0 & 12.8\% & 24.31 & 0.13 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.000 & 32.3 & 31.2 & 0 & 15.3\% & 0.53 & 0.12 \\
+FastaiMLP (D) & 889${}_{-81,+76}$ & 0.000 & 34.7 & 34.4 & 0 & 18.7\% & 7.09 & 0.84 \\
+Linear (T+E) & 488${}_{-191,+123}$ & 0.000 & 40.1 & 40.1 & 0 & 34.7\% & 149.75 & 0.13 \\
+KNN (T+E) & 441${}_{-97,+119}$ & 0.000 & 40.4 & 40.3 & 0 & 38.4\% & 2.43 & 0.18 \\
+Linear (T) & 426${}_{-160,+137}$ & 0.000 & 40.7 & 40.6 & 0 & 34.8\% & 149.75 & 0.06 \\
+Linear (D) & 350${}_{-139,+108}$ & 0.000 & 41.4 & 41.4 & 0 & 36.2\% & 3.45 & 0.09 \\
+KNN (T) & 334${}_{-138,+156}$ & 0.000 & 41.3 & 41.2 & 0 & 39.1\% & 2.43 & 0.03 \\
+KNN (D) & 8${}_{-219,+161}$ & 0.000 & 43.6 & 43.5 & 0 & 43.3\% & 0.04 & 0.03 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/tabpfn-reg/tabarena_leaderboard.csv b/data/tabpfn-reg/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..24fa248f82d18d730621b3f8a5168b13c3e3448e
--- /dev/null
+++ b/data/tabpfn-reg/tabarena_leaderboard.csv
@@ -0,0 +1,45 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
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+TABPFNV2 (tuned + ensemble),13720.707571309333,144.4128799729877,6347.594191591684,104.41432658354127,0.2415939115786189,0.3341509321234076,0.008040709459012918,0.018722358351318587,189200.55764735988,8406.36796869519,5.857142857142857,4.11819,6554.713698530197,35.598623005549115,6577.9935429270845,56.7950324715632,0.06610997682360657,0.25312173882346195,0.0006200613284135237,0.005184858759349278,150467.84733998418,5370.844516439232,2.0,3,2,0,2,1784.7,102.2,74.6,0.8870431893687708,0.5884711779448623
+AutoGluon 1.3 (4h),6519.395389483089,8.374968978715321,4847.228712217428,6.324376295240136,0.3085910704411772,0.3825220655079529,0.033251519267014036,0.049602203108293184,112121.1836885888,503.3434027862167,6.285714285714286,4.16484,5893.662480862936,2.3565427462259927,4737.606472356081,4.698652813671368,0.23133371369265834,0.32214544440356185,0.015883109174547605,0.03516384582661509,94686.22259252104,291.4795027754577,5.0,1,1,0,5,1767.5,84.1,73.3,0.8770764119601329,0.3248917748917749
+TABDPT (default),60.7062628246489,23.490844258051073,38.96865681756451,39.22338625261095,0.21328360616453232,0.31601375327884224,0.029680920262059787,0.014635575677645899,964.3036575691822,2256.3060022287436,6.428571428571429,4.26747,45.33390234311422,23.403914364178977,35.69555633627602,46.714399928500946,0.18149452492987356,0.3024909611245904,0.0039773225368111564,0.00227534979039058,904.9754515353668,2312.870048476579,4.0,3,0,0,4,1758.2,92.5,78.4,0.8737541528239202,0.49988662131519274
+CAT (tuned + ensemble),11360.685129829437,1.178349671288142,7479.02149818403,1.2406086528694698,0.3954429862751284,0.464509402409349,0.041752236791189334,0.05160245061199872,183467.75682165983,88.84726445881574,7.857142857142857,4.20863,12951.390986363092,0.6367310682932535,3558.1422644149734,0.9657383741190037,0.36849553490903875,0.5021915866656061,0.01735502485245788,0.056013107516843214,172301.38506843304,80.04598787510639,8.0,0,0,0,7,1702.9,95.0,73.9,0.840531561461794,0.1439255189255189
+CAT (tuned),11360.685129829437,0.18431518115694562,7479.02149818403,0.19033233887232168,0.42772839942623186,0.4880115582391967,0.04293561079285979,0.05673883273885333,183467.75682165983,14.175855537943391,9.0,4.23064,12951.390986363092,0.0923632542292277,3558.1422644149734,0.10450043094654878,0.5120020111687084,0.4970112300410973,0.018380899773098314,0.05607775104310802,172301.38506843304,9.480941702142749,10.0,0,0,1,6,1664.8,80.2,71.1,0.813953488372093,0.13734955520669806
+GBM (tuned + ensemble),1550.5350551957176,11.328482860232157,1095.0718838551823,8.219062398413474,0.48543215706848314,0.5433795474282531,0.054708320326180546,0.06759479279110976,25605.476583745745,681.2643137899079,10.285714285714286,4.21165,1305.722465435664,4.427071142196655,1003.1068347227413,4.891030532077303,0.3027896779514977,0.46595554262551947,0.02669651687138408,0.03428335611022812,24184.993298853384,538.7114397419949,7.0,0,0,0,7,1624.9,73.8,73.0,0.7840531561461794,0.12504095737178442
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+GBM (tuned),1550.5350551957176,1.4077377538832407,1095.0718838551823,1.2781273508842104,0.6186778290078211,0.6521109849275161,0.06336854992568382,0.08812536702511353,25605.476583745745,102.58551273372913,15.428571428571429,4.23482,1305.722465435664,0.5831021388371785,1003.1068347227413,0.8935510846842087,0.5552965190223861,0.567499678004668,0.027972044011269404,0.0771469045019801,24184.993298853384,73.3040824604662,14.0,0,0,0,7,1476.0,74.7,75.8,0.6644518272425249,0.07088017024991815
+CAT (default),34.406632555855644,0.13612949961707704,24.995110587630016,0.18681141105942162,0.6237900244125646,0.6752749432467652,0.06846154890412891,0.08126436753648981,594.4248060555409,11.797969177620454,15.571428571428571,4.21395,45.3870238383611,0.10361001491546631,10.909956325719385,0.1965340299278453,0.5847419575275037,0.6919434164192867,0.022724551212545796,0.08888361196746222,412.72250349454265,9.618489444383828,16.0,0,0,0,7,1461.4,84.7,58.1,0.6611295681063123,0.08003113832945766
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+XT (tuned + ensemble),660.5781686419533,1.1831508216403779,587.7119735330767,1.316998538867821,0.7590185126625372,0.7891367590549797,0.11152236742279202,0.09736117781705758,12611.439486121615,82.99538684812504,19.142857142857142,5.04794,628.4651173035304,0.7619378328323364,623.8510097889447,1.1438343022436304,1.0,0.9750636625508117,0.025903530399211117,0.10013551316301275,14081.344732130377,68.20231667792302,25.0,0,0,0,7,1368.7,96.2,56.9,0.5780730897009967,0.09159277504105091
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+NN_TORCH (tuned + ensemble),12800.476561368458,1.1380399908338272,7081.227309206272,1.497767305174234,0.8644212313341298,0.8606552018095075,0.08862343937037091,0.15385577166637224,182552.53268590602,90.9320692691531,23.571428571428573,4.65351,7304.863066792488,0.6995723327000936,5692.02320138285,1.353764106339938,1.0,0.8993834289137577,0.0931488267963303,0.09843675731093122,158151.6866749578,90.00555655937438,23.0,0,0,0,7,1259.7,84.5,68.0,0.4750830564784053,0.046582150653996304
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+NN_TORCH (tuned),12800.476561368458,0.08545414417509048,7081.227309206272,0.11421111903323287,0.8608314951688506,0.866065061374633,0.09394846170102965,0.1725237564760349,182552.53268590602,7.06193729144408,24.428571428571427,4.7148,7304.863066792488,0.06568752924601237,5692.02320138285,0.131112832826372,1.0,0.9520043501564557,0.09751345914708842,0.10970665592293476,158151.6866749578,6.1490644891314155,24.0,0,0,0,7,1236.5,77.6,69.9,0.45514950166112955,0.04688360293950356
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+NN_TORCH (default),45.35887446289971,0.0802082171515813,31.67622236459062,0.1165329153506545,0.9903234435493359,0.9704120580222819,0.12780665628037344,0.23160692294055926,751.5426969414585,7.0142840459527545,32.285714285714285,4.81722,35.183143361409506,0.06712730725606282,24.305650569576496,0.13416367617779604,1.0,0.9898941471678575,0.14511201063493562,0.13614151845721162,546.0088939544379,6.398331846186531,33.0,0,0,0,7,1002.7,81.2,78.2,0.2724252491694352,0.03227817407134295
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+KNN (tuned + ensemble),4.245547793025062,0.1591204832470606,2.4746151721359886,0.1895989937949328,1.0,0.991315763418419,0.3842177651885135,0.7048115612733296,62.40052816223211,11.931188445938428,40.42857142857143,8.41865,2.490966773033142,0.10710635185241699,2.4322953132177623,0.17676389939136214,1.0,1.0,0.41352429770943944,0.7907767115559304,56.15178324522979,13.124172379663548,41.0,0,0,0,7,441.2,118.9,96.3,0.08305647840531562,0.024796311969150473
+LR (tuned),152.34852822886572,0.037903934433346706,140.44301462951617,0.06382438080033122,1.0,1.0,0.34766987292821344,0.6285783845355511,2977.405199523172,3.6512091758703176,40.714285714285715,8.15927,150.0536302089691,0.03235630989074707,149.75412196503902,0.06193413547254449,1.0,1.0,0.2620998903501569,0.7430806606996537,3344.1604489764304,4.067640041721783,41.0,0,0,0,7,425.9,136.3,159.1,0.07641196013289037,0.024641310485245337
+KNN (tuned),4.245547793025062,0.02863398733593169,2.4746151721359886,0.030004193594300673,1.0,0.9977713176559868,0.3910594237799242,0.7476794241583206,62.40052816223211,1.9760324688350897,41.285714285714285,8.54422,2.490966773033142,0.014579184850056966,2.4322953132177623,0.02910016936139115,1.0,1.0,0.420237807146593,0.813866558903926,56.15178324522979,1.7112793648526077,42.0,0,0,0,7,334.4,155.7,137.5,0.06312292358803986,0.024270393591953304
+LR (default),3.9008307816490295,0.0549199554655287,3.239606778702032,0.07872063839931824,1.0,1.0,0.3619225843912718,0.6950518956016436,71.92078534572094,4.852563458042672,41.42857142857143,8.22758,3.3858322699864707,0.05012191931406657,3.447264865169665,0.08708413534655632,1.0,1.0,0.26184876537499124,0.7556433033178954,75.37476586504175,4.100500531123858,41.0,0,0,0,7,349.8,107.6,138.9,0.059800664451827246,0.02416543967066615
+KNN (default),0.06228995323181152,0.01962243337479849,0.04219198036913814,0.02726788157828385,1.0,0.9997734872659316,0.432705772179096,0.9384821422991884,1.0,1.6381446334470857,43.57142857142857,9.55646,0.04463104406992594,0.014348586400349935,0.043660770227879654,0.02863989301467053,1.0,1.0,0.4573857800165779,1.0,1.0,1.5792045889101338,44.0,0,0,0,7,8.2,160.2,218.5,0.009966777408637873,0.022964840038010772
diff --git a/data/tabpfn-reg/tuning-impact-elo-horizontal.pdf b/data/tabpfn-reg/tuning-impact-elo-horizontal.pdf
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new file mode 100644
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diff --git a/data/tabpfn-tabicl-cls/figures/critical-diagram.pdf b/data/tabpfn-tabicl-cls/figures/critical-diagram.pdf
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+version https://git-lfs.github.com/spec/v1
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diff --git a/data/tabpfn-tabicl-cls/leaderboard.tex b/data/tabpfn-tabicl-cls/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..b41ff65b5428b5f7a450717fe825914a05f0b501
--- /dev/null
+++ b/data/tabpfn-tabicl-cls/leaderboard.tex
@@ -0,0 +1,53 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+TabPFNv2 (T+E) & \textcolor{gold}{\textbf{1736${}_{-45,+46}$}} & \textcolor{gold}{\textbf{0.700}} & \textcolor{gold}{\textbf{5.8}} & \textcolor{gold}{\textbf{2.2}} & \textcolor{gold}{\textbf{8}} & \textcolor{gold}{\textbf{4.6\%}} & 3445.60 & 48.24 \\
+TabM (T+E) & \textcolor{silver}{\textbf{1612${}_{-37,+35}$}} & 0.483 & \textcolor{silver}{\textbf{9.3}} & 4.5 & 2 & 9.2\% & 2828.45 & 1.60 \\
+TabICL (D) & \textcolor{bronze}{\textbf{1608${}_{-34,+36}$}} & \textcolor{bronze}{\textbf{0.543}} & \textcolor{bronze}{\textbf{9.5}} & \textcolor{silver}{\textbf{3.3}} & \textcolor{silver}{\textbf{4}} & \textcolor{silver}{\textbf{7.1\%}} & 8.89 & 1.74 \\
+TabPFNv2 (T) & 1588${}_{-42,+32}$ & \textcolor{silver}{\textbf{0.544}} & 10.3 & 3.7 & 1 & \textcolor{bronze}{\textbf{8.2\%}} & 3445.60 & 1.00 \\
+RealMLP (T+E) & 1573${}_{-37,+29}$ & 0.402 & 10.9 & 7.1 & 0 & 10.4\% & 5786.69 & 4.27 \\
+AutoGluon 1.3 (4h) & 1544${}_{-44,+34}$ & 0.440 & 11.9 & 5.6 & 1 & 9.5\% & 2309.21 & 2.55 \\
+TabPFNv2 (D) & 1542${}_{-38,+38}$ & 0.499 & 12.0 & \textcolor{bronze}{\textbf{3.4}} & \textcolor{silver}{\textbf{4}} & 9.5\% & 4.06 & 0.44 \\
+LightGBM (T+E) & 1521${}_{-35,+40}$ & 0.319 & 12.9 & 7.1 & 1 & 11.8\% & 647.56 & 1.72 \\
+TabM (T) & 1512${}_{-40,+30}$ & 0.387 & 13.3 & 7.5 & 0 & 10.4\% & 2828.45 & 0.22 \\
+LightGBM (T) & 1462${}_{-34,+33}$ & 0.262 & 15.3 & 13.3 & 0 & 12.2\% & 647.56 & 0.28 \\
+CatBoost (T+E) & 1456${}_{-32,+38}$ & 0.271 & 15.8 & 11.6 & 0 & 11.7\% & 1465.86 & 0.69 \\
+CatBoost (T) & 1435${}_{-32,+36}$ & 0.247 & 16.5 & 10.5 & 0 & 12.0\% & 1465.86 & 0.09 \\
+TabM (D) & 1429${}_{-32,+35}$ & 0.288 & 17.0 & 11.2 & 0 & 13.7\% & 10.42 & 0.15 \\
+ModernNCA (T) & 1426${}_{-30,+37}$ & 0.255 & 17.1 & 8.7 & 1 & 11.7\% & 5944.88 & 0.52 \\
+CatBoost (D) & 1425${}_{-35,+32}$ & 0.228 & 17.0 & 10.0 & 0 & 13.3\% & 5.72 & 0.11 \\
+XGBoost (T+E) & 1415${}_{-33,+36}$ & 0.222 & 17.5 & 14.0 & 0 & 13.1\% & 766.06 & 1.92 \\
+EBM (T+E) & 1383${}_{-33,+31}$ & 0.186 & 19.1 & 12.5 & 0 & 15.7\% & 1109.06 & 0.23 \\
+XGBoost (T) & 1379${}_{-31,+39}$ & 0.181 & 19.2 & 16.8 & 0 & 13.4\% & 766.06 & 0.28 \\
+RealMLP (T) & 1379${}_{-42,+36}$ & 0.201 & 19.3 & 14.8 & 0 & 13.4\% & 5786.69 & 0.25 \\
+ModernNCA (T+E) & 1375${}_{-38,+35}$ & 0.307 & 19.5 & 8.6 & 0 & 12.7\% & 5944.88 & 8.40 \\
+ModernNCA (D) & 1367${}_{-33,+31}$ & 0.183 & 19.8 & 10.0 & 1 & 15.1\% & 14.80 & 0.34 \\
+TorchMLP (T+E) & 1356${}_{-32,+33}$ & 0.199 & 20.3 & 15.0 & 0 & 13.3\% & 2862.05 & 2.16 \\
+FastaiMLP (T+E) & 1356${}_{-35,+32}$ & 0.230 & 20.2 & 11.2 & 0 & 15.7\% & 1358.63 & 8.07 \\
+TabDPT (D) & 1323${}_{-33,+24}$ & 0.258 & 22.0 & 6.1 & 2 & 14.8\% & 27.49 & 8.86 \\
+EBM (T) & 1320${}_{-41,+33}$ & 0.120 & 22.1 & 17.3 & 0 & 16.5\% & 1109.06 & 0.03 \\
+EBM (D) & 1299${}_{-35,+37}$ & 0.153 & 23.0 & 9.7 & 1 & 17.3\% & 5.28 & 0.08 \\
+FastaiMLP (T) & 1269${}_{-40,+38}$ & 0.113 & 24.5 & 19.1 & 0 & 17.2\% & 1358.63 & 0.90 \\
+ExtraTrees (T+E) & 1260${}_{-47,+27}$ & 0.102 & 25.0 & 17.4 & 0 & 18.2\% & 370.85 & 1.47 \\
+TorchMLP (T) & 1238${}_{-33,+33}$ & 0.086 & 25.8 & 22.0 & 0 & 15.9\% & 2862.05 & 0.15 \\
+RealMLP (D) & 1234${}_{-36,+37}$ & 0.083 & 26.1 & 21.8 & 0 & 15.9\% & 36.24 & 0.28 \\
+XGBoost (D) & 1213${}_{-32,+41}$ & 0.032 & 27.1 & 24.8 & 0 & 16.7\% & 2.40 & 0.22 \\
+ExtraTrees (T) & 1200${}_{-32,+35}$ & 0.072 & 27.5 & 22.6 & 0 & 19.4\% & 370.85 & 0.16 \\
+RandomForest (T+E) & 1157${}_{-30,+36}$ & 0.057 & 29.5 & 22.0 & 0 & 19.5\% & 527.42 & 1.39 \\
+LightGBM (D) & 1154${}_{-29,+39}$ & 0.030 & 29.6 & 27.7 & 0 & 17.7\% & 2.90 & 0.13 \\
+RandomForest (T) & 1096${}_{-31,+36}$ & 0.023 & 31.9 & 29.1 & 0 & 20.8\% & 527.42 & 0.12 \\
+TorchMLP (D) & 1082${}_{-34,+36}$ & 0.030 & 32.3 & 28.8 & 0 & 21.4\% & 10.38 & 0.19 \\
+FastaiMLP (D) & 1051${}_{-33,+31}$ & 0.019 & 33.6 & 31.0 & 0 & 23.0\% & 4.73 & 0.62 \\
+Linear (T+E) & 1030${}_{-41,+39}$ & 0.055 & 34.2 & 22.9 & 0 & 28.3\% & 88.63 & 0.26 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.005 & 35.3 & 33.4 & 0 & 25.8\% & 0.45 & 0.07 \\
+Linear (T) & 997${}_{-41,+37}$ & 0.035 & 35.2 & 28.4 & 0 & 29.0\% & 88.63 & 0.09 \\
+Linear (D) & 982${}_{-39,+42}$ & 0.025 & 35.7 & 25.0 & 0 & 30.5\% & 2.27 & 0.11 \\
+ExtraTrees (D) & 956${}_{-51,+44}$ & 0.015 & 36.7 & 32.9 & 0 & 27.5\% & 0.40 & 0.07 \\
+KNN (T+E) & 704${}_{-46,+50}$ & 0.000 & 41.6 & 41.2 & 0 & 48.0\% & 3.71 & 0.17 \\
+KNN (T) & 609${}_{-48,+53}$ & 0.000 & 42.7 & 42.5 & 0 & 50.1\% & 3.71 & 0.04 \\
+KNN (D) & 408${}_{-96,+87}$ & 0.000 & 44.2 & 44.1 & 0 & 59.2\% & 0.07 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/tabpfn-tabicl-cls/tabarena_leaderboard.csv b/data/tabpfn-tabicl-cls/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9cfacb0044514fd9fcb6b0fd592ac532d061c310
--- /dev/null
+++ b/data/tabpfn-tabicl-cls/tabarena_leaderboard.csv
@@ -0,0 +1,46 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+TABPFNV2 (tuned + ensemble),14614.639715819583,148.11643430860633,3877.419600731425,65.38306906011915,0.2999799624009034,0.36592302109392316,0.046199591967015556,0.028953147052208875,72438.91792383758,5032.936436246271,5.788461538461538,0.17261500000000002,8173.816975453165,60.12680508957969,3445.602606086448,48.23597351862546,0.21441671773452015,0.4068697696081146,0.021640889295981347,0.016874625134937742,43032.16406688419,2931.913108083719,3.5,8,4,1,13,1736.3,45.7,44.6,0.8911713286713286,0.46269305993659526
+TABM (tuned + ensemble),7080.034926002046,2.290320276704609,3436.5218404284533,2.1079046445931953,0.5166525238887076,0.547775172494987,0.09238776741660952,0.04985850997225548,43123.835967101855,114.22499982092596,9.307692307692308,0.17379499999999998,6405.6722231189415,1.880999751885732,2828.4486752645407,1.5957955528425738,0.47136645123500964,0.5540981099375837,0.04617144277378826,0.034316541138176816,44088.87353980956,109.46959390379024,8.0,2,1,1,22,1612.5,34.1,36.5,0.8111888111888111,0.22027167779430223
+TABICL (default),21.933824618351764,2.7954679915028757,10.278805574502684,2.035731958506663,0.45732829959408833,0.5181742657898207,0.07108757868512468,0.03468928052253071,145.15967621212897,119.47187491598106,9.48076923076923,0.17207,19.54939921696981,2.1857474181387158,8.891901835466445,1.7433667301105085,0.46959481282562443,0.583210155619012,0.03487584587193143,0.02062556592021359,120.41628593220491,98.62751622705991,8.0,4,3,1,18,1607.5,35.1,33.8,0.8072552447552448,0.3008875223205248
+TABPFNV2 (tuned),14614.639715819583,4.849548537201351,3877.419600731425,2.254088269272259,0.4562783530638654,0.48513499648162556,0.08179742986099706,0.05007438478688923,72438.91792383758,165.31166707386078,10.326923076923077,0.1868,8173.816975453165,0.8871031337314181,3445.602606086448,0.9952991538273057,0.34109768962419973,0.46901679172287375,0.05804204088839793,0.02327176321840322,43032.16406688419,48.819771718398094,6.0,1,8,1,16,1588.5,31.1,41.1,0.7880244755244755,0.26951399319966235
+REALMLP (tuned + ensemble),17809.543566583365,5.343982117807764,5995.031437071161,5.920653730744878,0.5983187605703805,0.5877560111308218,0.10395608954668528,0.05250154829922825,112219.73072981052,290.95193637081104,10.865384615384615,0.17424,10344.939689996507,4.694504770967695,5786.691668992368,4.267078452745884,0.5696681015084324,0.5635752491354304,0.051850568481074444,0.04632297855309826,87871.6182808383,267.1363813998378,9.0,0,0,3,23,1572.9,29.0,36.1,0.7757867132867133,0.1417961559305308
+AutoGluon 1.3 (4h),5931.009838958377,3.1656774007357082,3673.8246676849526,3.0250106370782723,0.5599854226187498,0.5522297755522069,0.09461405000435809,0.05278876561410328,39712.28933278582,151.42916243848958,11.884615384615385,0.17691,5020.2655313505065,2.664059625731574,2309.213100138395,2.5543334551748154,0.5600808158121042,0.565709489217623,0.04762778419021996,0.04116173129591805,35357.34871646478,128.98489015193604,8.5,1,2,0,23,1544.1,33.4,44.0,0.7526223776223776,0.17858871441651328
+TABPFNV2 (default),11.884418644151115,1.1739113624279316,5.514520630749067,0.6490305586450037,0.5013642769155608,0.5669313754038009,0.09512311589648129,0.06286359741165812,76.29505964660768,41.758850754001486,12.038461538461538,0.1886,8.093020015292698,0.44081105126274955,4.062007578219401,0.4356807523460313,0.3771347187714357,0.5238664820190138,0.05018180969629027,0.021765272352146064,58.62232186481185,25.39697585954705,5.0,4,1,4,17,1541.6,37.1,37.8,0.7491258741258742,0.2935579164181083
+GBM (tuned + ensemble),1379.7589400635827,3.041326452829899,860.8328230742596,2.952036385308545,0.6806482110513491,0.6837279086393804,0.1176313686476465,0.061906173521871284,10455.248725429481,156.11534656534064,12.903846153846153,0.174085,1321.8940714677174,2.4085231754514904,647.5566852470606,1.7204157994123173,0.7005535661954161,0.7073670918352166,0.051567085599247775,0.05140473915859429,9480.825611975059,103.35126359750211,12.5,1,0,2,23,1521.0,39.2,34.8,0.729458041958042,0.14042554762228465
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+RF (default),0.9640304686676744,0.07426402477117686,0.5084719578868067,0.0844146006139,0.9951894125278877,0.9701912114787967,0.25755513106395067,0.2479832267773854,6.6754175478812146,4.153857105918795,35.28846153846154,0.213105,0.8910642200046115,0.05825435982810126,0.447836563000179,0.06594795127086661,1.0,1.0,0.17586669886220047,0.11692775501298655,5.654732996519019,3.502947097147378,37.5,0,0,0,26,1000.0,0.0,0.0,0.22071678321678323,0.029920050824686344
+LR (default),4.707631549162742,0.1483236100938585,2.804518891637732,0.1437661673750307,0.9745259999639975,0.9520629989740693,0.305476365274656,0.29332097764816845,36.73103788870433,7.308777223189359,35.67307692307692,0.22103499999999998,4.746849238872528,0.08430976470311483,2.2657084486770183,0.10642460584640503,1.0,1.0,0.22930816077470617,0.22370793213879017,31.50980214431158,4.703818974402271,39.5,0,0,1,25,982.3,41.6,38.5,0.21197552447552448,0.03995938942367327
+XT (default),0.8462119445841536,0.0803421718442542,0.47456795510527094,0.08795126020679146,0.9851460583683391,0.9642266228854909,0.2753142737967973,0.27671727525790224,6.0304600748852675,4.406716139948246,36.65384615384615,0.215405,0.7635703219307794,0.06673479080200195,0.40395335630596185,0.07007418015238884,1.0,1.0,0.18424442460299273,0.1562338348801046,5.148113375558061,3.7424721126192138,39.0,0,0,0,26,955.5,43.9,50.8,0.1896853146853147,0.030378669430214802
+KNN (tuned + ensemble),16.440937072497146,0.2739423143558013,7.036183733958932,0.19964653999651732,1.0,0.9945521006475282,0.47993514547383553,0.5914832997832359,57.836601086323085,11.285407568076662,41.57692307692308,0.31118999999999997,6.799899099932777,0.1030390567249722,3.707274221912526,0.16730320161416595,1.0,1.0,0.4552264030449861,0.6183873456591064,55.06974575171796,8.266636963631449,43.0,0,0,0,26,703.5,49.4,45.5,0.0777972027972028,0.024279443329967162
+KNN (tuned),16.440937072497146,0.06075953357240074,7.036183733958932,0.04304950843110991,1.0,0.9966557577121117,0.5010874288021712,0.6455640735444151,57.836601086323085,2.5027008595459077,42.69230769230769,0.31467999999999996,6.799899099932777,0.036990099483066134,3.707274221912526,0.039402452723266784,1.0,1.0,0.4955223463378593,0.6729856958219811,55.06974575171796,2.091430487051362,44.0,0,0,0,26,609.2,52.7,48.0,0.05244755244755245,0.023502270498812147
+KNN (default),0.2899092884145231,0.0317453768518236,0.13426353967394633,0.031065835648244968,1.0,1.0,0.5923454279012129,0.9491257378249622,1.00065410372272,1.4527699655062962,44.25,0.34582,0.1258228341738383,0.02117468251122369,0.07457399441809831,0.021006283652748647,1.0,1.0,0.6201400300158697,1.0,1.0,1.0057335151210618,45.0,0,0,0,26,407.8,86.4,95.3,0.017045454545454544,0.022663376691509682
diff --git a/data/tabpfn-tabicl-cls/tuning-impact-elo-horizontal.pdf b/data/tabpfn-tabicl-cls/tuning-impact-elo-horizontal.pdf
new file mode 100644
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diff --git a/data/tabpfn-tabicl-cls/tuning-impact-elo-horizontal.png.zip b/data/tabpfn-tabicl-cls/tuning-impact-elo-horizontal.png.zip
new file mode 100644
index 0000000000000000000000000000000000000000..4f6908fba6c41ba3576608b66bc23a0647bbfbc2
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+size 139645
diff --git a/data/tabpfn-tabicl-imputed-cls/figures/critical-diagram.pdf b/data/tabpfn-tabicl-imputed-cls/figures/critical-diagram.pdf
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diff --git a/data/tabpfn-tabicl-imputed-cls/figures/critical-diagram.png.zip b/data/tabpfn-tabicl-imputed-cls/figures/critical-diagram.png.zip
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index 0000000000000000000000000000000000000000..24e08506593fee7a8727aefce7940752470d6e76
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+version https://git-lfs.github.com/spec/v1
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diff --git a/data/tabpfn-tabicl-imputed-cls/leaderboard.tex b/data/tabpfn-tabicl-imputed-cls/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..b41ff65b5428b5f7a450717fe825914a05f0b501
--- /dev/null
+++ b/data/tabpfn-tabicl-imputed-cls/leaderboard.tex
@@ -0,0 +1,53 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+TabPFNv2 (T+E) & \textcolor{gold}{\textbf{1736${}_{-45,+46}$}} & \textcolor{gold}{\textbf{0.700}} & \textcolor{gold}{\textbf{5.8}} & \textcolor{gold}{\textbf{2.2}} & \textcolor{gold}{\textbf{8}} & \textcolor{gold}{\textbf{4.6\%}} & 3445.60 & 48.24 \\
+TabM (T+E) & \textcolor{silver}{\textbf{1612${}_{-37,+35}$}} & 0.483 & \textcolor{silver}{\textbf{9.3}} & 4.5 & 2 & 9.2\% & 2828.45 & 1.60 \\
+TabICL (D) & \textcolor{bronze}{\textbf{1608${}_{-34,+36}$}} & \textcolor{bronze}{\textbf{0.543}} & \textcolor{bronze}{\textbf{9.5}} & \textcolor{silver}{\textbf{3.3}} & \textcolor{silver}{\textbf{4}} & \textcolor{silver}{\textbf{7.1\%}} & 8.89 & 1.74 \\
+TabPFNv2 (T) & 1588${}_{-42,+32}$ & \textcolor{silver}{\textbf{0.544}} & 10.3 & 3.7 & 1 & \textcolor{bronze}{\textbf{8.2\%}} & 3445.60 & 1.00 \\
+RealMLP (T+E) & 1573${}_{-37,+29}$ & 0.402 & 10.9 & 7.1 & 0 & 10.4\% & 5786.69 & 4.27 \\
+AutoGluon 1.3 (4h) & 1544${}_{-44,+34}$ & 0.440 & 11.9 & 5.6 & 1 & 9.5\% & 2309.21 & 2.55 \\
+TabPFNv2 (D) & 1542${}_{-38,+38}$ & 0.499 & 12.0 & \textcolor{bronze}{\textbf{3.4}} & \textcolor{silver}{\textbf{4}} & 9.5\% & 4.06 & 0.44 \\
+LightGBM (T+E) & 1521${}_{-35,+40}$ & 0.319 & 12.9 & 7.1 & 1 & 11.8\% & 647.56 & 1.72 \\
+TabM (T) & 1512${}_{-40,+30}$ & 0.387 & 13.3 & 7.5 & 0 & 10.4\% & 2828.45 & 0.22 \\
+LightGBM (T) & 1462${}_{-34,+33}$ & 0.262 & 15.3 & 13.3 & 0 & 12.2\% & 647.56 & 0.28 \\
+CatBoost (T+E) & 1456${}_{-32,+38}$ & 0.271 & 15.8 & 11.6 & 0 & 11.7\% & 1465.86 & 0.69 \\
+CatBoost (T) & 1435${}_{-32,+36}$ & 0.247 & 16.5 & 10.5 & 0 & 12.0\% & 1465.86 & 0.09 \\
+TabM (D) & 1429${}_{-32,+35}$ & 0.288 & 17.0 & 11.2 & 0 & 13.7\% & 10.42 & 0.15 \\
+ModernNCA (T) & 1426${}_{-30,+37}$ & 0.255 & 17.1 & 8.7 & 1 & 11.7\% & 5944.88 & 0.52 \\
+CatBoost (D) & 1425${}_{-35,+32}$ & 0.228 & 17.0 & 10.0 & 0 & 13.3\% & 5.72 & 0.11 \\
+XGBoost (T+E) & 1415${}_{-33,+36}$ & 0.222 & 17.5 & 14.0 & 0 & 13.1\% & 766.06 & 1.92 \\
+EBM (T+E) & 1383${}_{-33,+31}$ & 0.186 & 19.1 & 12.5 & 0 & 15.7\% & 1109.06 & 0.23 \\
+XGBoost (T) & 1379${}_{-31,+39}$ & 0.181 & 19.2 & 16.8 & 0 & 13.4\% & 766.06 & 0.28 \\
+RealMLP (T) & 1379${}_{-42,+36}$ & 0.201 & 19.3 & 14.8 & 0 & 13.4\% & 5786.69 & 0.25 \\
+ModernNCA (T+E) & 1375${}_{-38,+35}$ & 0.307 & 19.5 & 8.6 & 0 & 12.7\% & 5944.88 & 8.40 \\
+ModernNCA (D) & 1367${}_{-33,+31}$ & 0.183 & 19.8 & 10.0 & 1 & 15.1\% & 14.80 & 0.34 \\
+TorchMLP (T+E) & 1356${}_{-32,+33}$ & 0.199 & 20.3 & 15.0 & 0 & 13.3\% & 2862.05 & 2.16 \\
+FastaiMLP (T+E) & 1356${}_{-35,+32}$ & 0.230 & 20.2 & 11.2 & 0 & 15.7\% & 1358.63 & 8.07 \\
+TabDPT (D) & 1323${}_{-33,+24}$ & 0.258 & 22.0 & 6.1 & 2 & 14.8\% & 27.49 & 8.86 \\
+EBM (T) & 1320${}_{-41,+33}$ & 0.120 & 22.1 & 17.3 & 0 & 16.5\% & 1109.06 & 0.03 \\
+EBM (D) & 1299${}_{-35,+37}$ & 0.153 & 23.0 & 9.7 & 1 & 17.3\% & 5.28 & 0.08 \\
+FastaiMLP (T) & 1269${}_{-40,+38}$ & 0.113 & 24.5 & 19.1 & 0 & 17.2\% & 1358.63 & 0.90 \\
+ExtraTrees (T+E) & 1260${}_{-47,+27}$ & 0.102 & 25.0 & 17.4 & 0 & 18.2\% & 370.85 & 1.47 \\
+TorchMLP (T) & 1238${}_{-33,+33}$ & 0.086 & 25.8 & 22.0 & 0 & 15.9\% & 2862.05 & 0.15 \\
+RealMLP (D) & 1234${}_{-36,+37}$ & 0.083 & 26.1 & 21.8 & 0 & 15.9\% & 36.24 & 0.28 \\
+XGBoost (D) & 1213${}_{-32,+41}$ & 0.032 & 27.1 & 24.8 & 0 & 16.7\% & 2.40 & 0.22 \\
+ExtraTrees (T) & 1200${}_{-32,+35}$ & 0.072 & 27.5 & 22.6 & 0 & 19.4\% & 370.85 & 0.16 \\
+RandomForest (T+E) & 1157${}_{-30,+36}$ & 0.057 & 29.5 & 22.0 & 0 & 19.5\% & 527.42 & 1.39 \\
+LightGBM (D) & 1154${}_{-29,+39}$ & 0.030 & 29.6 & 27.7 & 0 & 17.7\% & 2.90 & 0.13 \\
+RandomForest (T) & 1096${}_{-31,+36}$ & 0.023 & 31.9 & 29.1 & 0 & 20.8\% & 527.42 & 0.12 \\
+TorchMLP (D) & 1082${}_{-34,+36}$ & 0.030 & 32.3 & 28.8 & 0 & 21.4\% & 10.38 & 0.19 \\
+FastaiMLP (D) & 1051${}_{-33,+31}$ & 0.019 & 33.6 & 31.0 & 0 & 23.0\% & 4.73 & 0.62 \\
+Linear (T+E) & 1030${}_{-41,+39}$ & 0.055 & 34.2 & 22.9 & 0 & 28.3\% & 88.63 & 0.26 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.005 & 35.3 & 33.4 & 0 & 25.8\% & 0.45 & 0.07 \\
+Linear (T) & 997${}_{-41,+37}$ & 0.035 & 35.2 & 28.4 & 0 & 29.0\% & 88.63 & 0.09 \\
+Linear (D) & 982${}_{-39,+42}$ & 0.025 & 35.7 & 25.0 & 0 & 30.5\% & 2.27 & 0.11 \\
+ExtraTrees (D) & 956${}_{-51,+44}$ & 0.015 & 36.7 & 32.9 & 0 & 27.5\% & 0.40 & 0.07 \\
+KNN (T+E) & 704${}_{-46,+50}$ & 0.000 & 41.6 & 41.2 & 0 & 48.0\% & 3.71 & 0.17 \\
+KNN (T) & 609${}_{-48,+53}$ & 0.000 & 42.7 & 42.5 & 0 & 50.1\% & 3.71 & 0.04 \\
+KNN (D) & 408${}_{-96,+87}$ & 0.000 & 44.2 & 44.1 & 0 & 59.2\% & 0.07 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/tabpfn-tabicl-imputed-cls/tabarena_leaderboard.csv b/data/tabpfn-tabicl-imputed-cls/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9cfacb0044514fd9fcb6b0fd592ac532d061c310
--- /dev/null
+++ b/data/tabpfn-tabicl-imputed-cls/tabarena_leaderboard.csv
@@ -0,0 +1,46 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
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+TABM (tuned + ensemble),7080.034926002046,2.290320276704609,3436.5218404284533,2.1079046445931953,0.5166525238887076,0.547775172494987,0.09238776741660952,0.04985850997225548,43123.835967101855,114.22499982092596,9.307692307692308,0.17379499999999998,6405.6722231189415,1.880999751885732,2828.4486752645407,1.5957955528425738,0.47136645123500964,0.5540981099375837,0.04617144277378826,0.034316541138176816,44088.87353980956,109.46959390379024,8.0,2,1,1,22,1612.5,34.1,36.5,0.8111888111888111,0.22027167779430223
+TABICL (default),21.933824618351764,2.7954679915028757,10.278805574502684,2.035731958506663,0.45732829959408833,0.5181742657898207,0.07108757868512468,0.03468928052253071,145.15967621212897,119.47187491598106,9.48076923076923,0.17207,19.54939921696981,2.1857474181387158,8.891901835466445,1.7433667301105085,0.46959481282562443,0.583210155619012,0.03487584587193143,0.02062556592021359,120.41628593220491,98.62751622705991,8.0,4,3,1,18,1607.5,35.1,33.8,0.8072552447552448,0.3008875223205248
+TABPFNV2 (tuned),14614.639715819583,4.849548537201351,3877.419600731425,2.254088269272259,0.4562783530638654,0.48513499648162556,0.08179742986099706,0.05007438478688923,72438.91792383758,165.31166707386078,10.326923076923077,0.1868,8173.816975453165,0.8871031337314181,3445.602606086448,0.9952991538273057,0.34109768962419973,0.46901679172287375,0.05804204088839793,0.02327176321840322,43032.16406688419,48.819771718398094,6.0,1,8,1,16,1588.5,31.1,41.1,0.7880244755244755,0.26951399319966235
+REALMLP (tuned + ensemble),17809.543566583365,5.343982117807764,5995.031437071161,5.920653730744878,0.5983187605703805,0.5877560111308218,0.10395608954668528,0.05250154829922825,112219.73072981052,290.95193637081104,10.865384615384615,0.17424,10344.939689996507,4.694504770967695,5786.691668992368,4.267078452745884,0.5696681015084324,0.5635752491354304,0.051850568481074444,0.04632297855309826,87871.6182808383,267.1363813998378,9.0,0,0,3,23,1572.9,29.0,36.1,0.7757867132867133,0.1417961559305308
+AutoGluon 1.3 (4h),5931.009838958377,3.1656774007357082,3673.8246676849526,3.0250106370782723,0.5599854226187498,0.5522297755522069,0.09461405000435809,0.05278876561410328,39712.28933278582,151.42916243848958,11.884615384615385,0.17691,5020.2655313505065,2.664059625731574,2309.213100138395,2.5543334551748154,0.5600808158121042,0.565709489217623,0.04762778419021996,0.04116173129591805,35357.34871646478,128.98489015193604,8.5,1,2,0,23,1544.1,33.4,44.0,0.7526223776223776,0.17858871441651328
+TABPFNV2 (default),11.884418644151115,1.1739113624279316,5.514520630749067,0.6490305586450037,0.5013642769155608,0.5669313754038009,0.09512311589648129,0.06286359741165812,76.29505964660768,41.758850754001486,12.038461538461538,0.1886,8.093020015292698,0.44081105126274955,4.062007578219401,0.4356807523460313,0.3771347187714357,0.5238664820190138,0.05018180969629027,0.021765272352146064,58.62232186481185,25.39697585954705,5.0,4,1,4,17,1541.6,37.1,37.8,0.7491258741258742,0.2935579164181083
+GBM (tuned + ensemble),1379.7589400635827,3.041326452829899,860.8328230742596,2.952036385308545,0.6806482110513491,0.6837279086393804,0.1176313686476465,0.061906173521871284,10455.248725429481,156.11534656534064,12.903846153846153,0.174085,1321.8940714677174,2.4085231754514904,647.5566852470606,1.7204157994123173,0.7005535661954161,0.7073670918352166,0.051567085599247775,0.05140473915859429,9480.825611975059,103.35126359750211,12.5,1,0,2,23,1521.0,39.2,34.8,0.729458041958042,0.14042554762228465
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+CAT (tuned + ensemble),4698.536897969144,0.9597997341400538,2797.035390366479,0.7965719365801648,0.728920075896456,0.7126569304674935,0.11678549305379037,0.07069284985664498,27448.91121504087,44.04781428274726,15.76923076923077,0.1771,3111.985966463884,0.7725222905476887,1465.8584785724734,0.6919304562740058,0.7992439551459185,0.720606902542752,0.0655088388746699,0.053959365141496254,21194.263639925673,39.04121549244424,16.0,0,0,1,25,1455.5,38.0,31.7,0.6643356643356644,0.08640388177736971
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+LR (tuned),175.85719627702338,0.1364539579448537,115.79203038641191,0.12123112010680681,0.9645430999513104,0.943877751101422,0.2902155463327537,0.25773756332102926,1448.7019616321465,6.009867470278231,35.21153846153846,0.21681,158.39292872746785,0.07476819356282552,88.63237206036187,0.08838151119168902,1.0,1.0,0.22518752798442315,0.1834788883245299,1246.7830478036524,4.016028876859062,38.0,0,0,0,26,996.9,36.4,40.6,0.22246503496503497,0.03516566041445386
+RF (default),0.9640304686676744,0.07426402477117686,0.5084719578868067,0.0844146006139,0.9951894125278877,0.9701912114787967,0.25755513106395067,0.2479832267773854,6.6754175478812146,4.153857105918795,35.28846153846154,0.213105,0.8910642200046115,0.05825435982810126,0.447836563000179,0.06594795127086661,1.0,1.0,0.17586669886220047,0.11692775501298655,5.654732996519019,3.502947097147378,37.5,0,0,0,26,1000.0,0.0,0.0,0.22071678321678323,0.029920050824686344
+LR (default),4.707631549162742,0.1483236100938585,2.804518891637732,0.1437661673750307,0.9745259999639975,0.9520629989740693,0.305476365274656,0.29332097764816845,36.73103788870433,7.308777223189359,35.67307692307692,0.22103499999999998,4.746849238872528,0.08430976470311483,2.2657084486770183,0.10642460584640503,1.0,1.0,0.22930816077470617,0.22370793213879017,31.50980214431158,4.703818974402271,39.5,0,0,1,25,982.3,41.6,38.5,0.21197552447552448,0.03995938942367327
+XT (default),0.8462119445841536,0.0803421718442542,0.47456795510527094,0.08795126020679146,0.9851460583683391,0.9642266228854909,0.2753142737967973,0.27671727525790224,6.0304600748852675,4.406716139948246,36.65384615384615,0.215405,0.7635703219307794,0.06673479080200195,0.40395335630596185,0.07007418015238884,1.0,1.0,0.18424442460299273,0.1562338348801046,5.148113375558061,3.7424721126192138,39.0,0,0,0,26,955.5,43.9,50.8,0.1896853146853147,0.030378669430214802
+KNN (tuned + ensemble),16.440937072497146,0.2739423143558013,7.036183733958932,0.19964653999651732,1.0,0.9945521006475282,0.47993514547383553,0.5914832997832359,57.836601086323085,11.285407568076662,41.57692307692308,0.31118999999999997,6.799899099932777,0.1030390567249722,3.707274221912526,0.16730320161416595,1.0,1.0,0.4552264030449861,0.6183873456591064,55.06974575171796,8.266636963631449,43.0,0,0,0,26,703.5,49.4,45.5,0.0777972027972028,0.024279443329967162
+KNN (tuned),16.440937072497146,0.06075953357240074,7.036183733958932,0.04304950843110991,1.0,0.9966557577121117,0.5010874288021712,0.6455640735444151,57.836601086323085,2.5027008595459077,42.69230769230769,0.31467999999999996,6.799899099932777,0.036990099483066134,3.707274221912526,0.039402452723266784,1.0,1.0,0.4955223463378593,0.6729856958219811,55.06974575171796,2.091430487051362,44.0,0,0,0,26,609.2,52.7,48.0,0.05244755244755245,0.023502270498812147
+KNN (default),0.2899092884145231,0.0317453768518236,0.13426353967394633,0.031065835648244968,1.0,1.0,0.5923454279012129,0.9491257378249622,1.00065410372272,1.4527699655062962,44.25,0.34582,0.1258228341738383,0.02117468251122369,0.07457399441809831,0.021006283652748647,1.0,1.0,0.6201400300158697,1.0,1.0,1.0057335151210618,45.0,0,0,0,26,407.8,86.4,95.3,0.017045454545454544,0.022663376691509682
diff --git a/data/tabpfn-tabicl-imputed-cls/tuning-impact-elo-horizontal.pdf b/data/tabpfn-tabicl-imputed-cls/tuning-impact-elo-horizontal.pdf
new file mode 100644
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diff --git a/data/tabpfn-tabicl-imputed-cls/tuning-impact-elo-horizontal.png.zip b/data/tabpfn-tabicl-imputed-cls/tuning-impact-elo-horizontal.png.zip
new file mode 100644
index 0000000000000000000000000000000000000000..4f6908fba6c41ba3576608b66bc23a0647bbfbc2
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+size 139645
diff --git a/data/tabpfn-tabicl-imputed/figures/critical-diagram.pdf b/data/tabpfn-tabicl-imputed/figures/critical-diagram.pdf
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diff --git a/data/tabpfn-tabicl-imputed/leaderboard.tex b/data/tabpfn-tabicl-imputed/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..b41ff65b5428b5f7a450717fe825914a05f0b501
--- /dev/null
+++ b/data/tabpfn-tabicl-imputed/leaderboard.tex
@@ -0,0 +1,53 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+TabPFNv2 (T+E) & \textcolor{gold}{\textbf{1736${}_{-45,+46}$}} & \textcolor{gold}{\textbf{0.700}} & \textcolor{gold}{\textbf{5.8}} & \textcolor{gold}{\textbf{2.2}} & \textcolor{gold}{\textbf{8}} & \textcolor{gold}{\textbf{4.6\%}} & 3445.60 & 48.24 \\
+TabM (T+E) & \textcolor{silver}{\textbf{1612${}_{-37,+35}$}} & 0.483 & \textcolor{silver}{\textbf{9.3}} & 4.5 & 2 & 9.2\% & 2828.45 & 1.60 \\
+TabICL (D) & \textcolor{bronze}{\textbf{1608${}_{-34,+36}$}} & \textcolor{bronze}{\textbf{0.543}} & \textcolor{bronze}{\textbf{9.5}} & \textcolor{silver}{\textbf{3.3}} & \textcolor{silver}{\textbf{4}} & \textcolor{silver}{\textbf{7.1\%}} & 8.89 & 1.74 \\
+TabPFNv2 (T) & 1588${}_{-42,+32}$ & \textcolor{silver}{\textbf{0.544}} & 10.3 & 3.7 & 1 & \textcolor{bronze}{\textbf{8.2\%}} & 3445.60 & 1.00 \\
+RealMLP (T+E) & 1573${}_{-37,+29}$ & 0.402 & 10.9 & 7.1 & 0 & 10.4\% & 5786.69 & 4.27 \\
+AutoGluon 1.3 (4h) & 1544${}_{-44,+34}$ & 0.440 & 11.9 & 5.6 & 1 & 9.5\% & 2309.21 & 2.55 \\
+TabPFNv2 (D) & 1542${}_{-38,+38}$ & 0.499 & 12.0 & \textcolor{bronze}{\textbf{3.4}} & \textcolor{silver}{\textbf{4}} & 9.5\% & 4.06 & 0.44 \\
+LightGBM (T+E) & 1521${}_{-35,+40}$ & 0.319 & 12.9 & 7.1 & 1 & 11.8\% & 647.56 & 1.72 \\
+TabM (T) & 1512${}_{-40,+30}$ & 0.387 & 13.3 & 7.5 & 0 & 10.4\% & 2828.45 & 0.22 \\
+LightGBM (T) & 1462${}_{-34,+33}$ & 0.262 & 15.3 & 13.3 & 0 & 12.2\% & 647.56 & 0.28 \\
+CatBoost (T+E) & 1456${}_{-32,+38}$ & 0.271 & 15.8 & 11.6 & 0 & 11.7\% & 1465.86 & 0.69 \\
+CatBoost (T) & 1435${}_{-32,+36}$ & 0.247 & 16.5 & 10.5 & 0 & 12.0\% & 1465.86 & 0.09 \\
+TabM (D) & 1429${}_{-32,+35}$ & 0.288 & 17.0 & 11.2 & 0 & 13.7\% & 10.42 & 0.15 \\
+ModernNCA (T) & 1426${}_{-30,+37}$ & 0.255 & 17.1 & 8.7 & 1 & 11.7\% & 5944.88 & 0.52 \\
+CatBoost (D) & 1425${}_{-35,+32}$ & 0.228 & 17.0 & 10.0 & 0 & 13.3\% & 5.72 & 0.11 \\
+XGBoost (T+E) & 1415${}_{-33,+36}$ & 0.222 & 17.5 & 14.0 & 0 & 13.1\% & 766.06 & 1.92 \\
+EBM (T+E) & 1383${}_{-33,+31}$ & 0.186 & 19.1 & 12.5 & 0 & 15.7\% & 1109.06 & 0.23 \\
+XGBoost (T) & 1379${}_{-31,+39}$ & 0.181 & 19.2 & 16.8 & 0 & 13.4\% & 766.06 & 0.28 \\
+RealMLP (T) & 1379${}_{-42,+36}$ & 0.201 & 19.3 & 14.8 & 0 & 13.4\% & 5786.69 & 0.25 \\
+ModernNCA (T+E) & 1375${}_{-38,+35}$ & 0.307 & 19.5 & 8.6 & 0 & 12.7\% & 5944.88 & 8.40 \\
+ModernNCA (D) & 1367${}_{-33,+31}$ & 0.183 & 19.8 & 10.0 & 1 & 15.1\% & 14.80 & 0.34 \\
+TorchMLP (T+E) & 1356${}_{-32,+33}$ & 0.199 & 20.3 & 15.0 & 0 & 13.3\% & 2862.05 & 2.16 \\
+FastaiMLP (T+E) & 1356${}_{-35,+32}$ & 0.230 & 20.2 & 11.2 & 0 & 15.7\% & 1358.63 & 8.07 \\
+TabDPT (D) & 1323${}_{-33,+24}$ & 0.258 & 22.0 & 6.1 & 2 & 14.8\% & 27.49 & 8.86 \\
+EBM (T) & 1320${}_{-41,+33}$ & 0.120 & 22.1 & 17.3 & 0 & 16.5\% & 1109.06 & 0.03 \\
+EBM (D) & 1299${}_{-35,+37}$ & 0.153 & 23.0 & 9.7 & 1 & 17.3\% & 5.28 & 0.08 \\
+FastaiMLP (T) & 1269${}_{-40,+38}$ & 0.113 & 24.5 & 19.1 & 0 & 17.2\% & 1358.63 & 0.90 \\
+ExtraTrees (T+E) & 1260${}_{-47,+27}$ & 0.102 & 25.0 & 17.4 & 0 & 18.2\% & 370.85 & 1.47 \\
+TorchMLP (T) & 1238${}_{-33,+33}$ & 0.086 & 25.8 & 22.0 & 0 & 15.9\% & 2862.05 & 0.15 \\
+RealMLP (D) & 1234${}_{-36,+37}$ & 0.083 & 26.1 & 21.8 & 0 & 15.9\% & 36.24 & 0.28 \\
+XGBoost (D) & 1213${}_{-32,+41}$ & 0.032 & 27.1 & 24.8 & 0 & 16.7\% & 2.40 & 0.22 \\
+ExtraTrees (T) & 1200${}_{-32,+35}$ & 0.072 & 27.5 & 22.6 & 0 & 19.4\% & 370.85 & 0.16 \\
+RandomForest (T+E) & 1157${}_{-30,+36}$ & 0.057 & 29.5 & 22.0 & 0 & 19.5\% & 527.42 & 1.39 \\
+LightGBM (D) & 1154${}_{-29,+39}$ & 0.030 & 29.6 & 27.7 & 0 & 17.7\% & 2.90 & 0.13 \\
+RandomForest (T) & 1096${}_{-31,+36}$ & 0.023 & 31.9 & 29.1 & 0 & 20.8\% & 527.42 & 0.12 \\
+TorchMLP (D) & 1082${}_{-34,+36}$ & 0.030 & 32.3 & 28.8 & 0 & 21.4\% & 10.38 & 0.19 \\
+FastaiMLP (D) & 1051${}_{-33,+31}$ & 0.019 & 33.6 & 31.0 & 0 & 23.0\% & 4.73 & 0.62 \\
+Linear (T+E) & 1030${}_{-41,+39}$ & 0.055 & 34.2 & 22.9 & 0 & 28.3\% & 88.63 & 0.26 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.005 & 35.3 & 33.4 & 0 & 25.8\% & 0.45 & 0.07 \\
+Linear (T) & 997${}_{-41,+37}$ & 0.035 & 35.2 & 28.4 & 0 & 29.0\% & 88.63 & 0.09 \\
+Linear (D) & 982${}_{-39,+42}$ & 0.025 & 35.7 & 25.0 & 0 & 30.5\% & 2.27 & 0.11 \\
+ExtraTrees (D) & 956${}_{-51,+44}$ & 0.015 & 36.7 & 32.9 & 0 & 27.5\% & 0.40 & 0.07 \\
+KNN (T+E) & 704${}_{-46,+50}$ & 0.000 & 41.6 & 41.2 & 0 & 48.0\% & 3.71 & 0.17 \\
+KNN (T) & 609${}_{-48,+53}$ & 0.000 & 42.7 & 42.5 & 0 & 50.1\% & 3.71 & 0.04 \\
+KNN (D) & 408${}_{-96,+87}$ & 0.000 & 44.2 & 44.1 & 0 & 59.2\% & 0.07 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/tabpfn-tabicl-imputed/tabarena_leaderboard.csv b/data/tabpfn-tabicl-imputed/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9cfacb0044514fd9fcb6b0fd592ac532d061c310
--- /dev/null
+++ b/data/tabpfn-tabicl-imputed/tabarena_leaderboard.csv
@@ -0,0 +1,46 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+TABPFNV2 (tuned + ensemble),14614.639715819583,148.11643430860633,3877.419600731425,65.38306906011915,0.2999799624009034,0.36592302109392316,0.046199591967015556,0.028953147052208875,72438.91792383758,5032.936436246271,5.788461538461538,0.17261500000000002,8173.816975453165,60.12680508957969,3445.602606086448,48.23597351862546,0.21441671773452015,0.4068697696081146,0.021640889295981347,0.016874625134937742,43032.16406688419,2931.913108083719,3.5,8,4,1,13,1736.3,45.7,44.6,0.8911713286713286,0.46269305993659526
+TABM (tuned + ensemble),7080.034926002046,2.290320276704609,3436.5218404284533,2.1079046445931953,0.5166525238887076,0.547775172494987,0.09238776741660952,0.04985850997225548,43123.835967101855,114.22499982092596,9.307692307692308,0.17379499999999998,6405.6722231189415,1.880999751885732,2828.4486752645407,1.5957955528425738,0.47136645123500964,0.5540981099375837,0.04617144277378826,0.034316541138176816,44088.87353980956,109.46959390379024,8.0,2,1,1,22,1612.5,34.1,36.5,0.8111888111888111,0.22027167779430223
+TABICL (default),21.933824618351764,2.7954679915028757,10.278805574502684,2.035731958506663,0.45732829959408833,0.5181742657898207,0.07108757868512468,0.03468928052253071,145.15967621212897,119.47187491598106,9.48076923076923,0.17207,19.54939921696981,2.1857474181387158,8.891901835466445,1.7433667301105085,0.46959481282562443,0.583210155619012,0.03487584587193143,0.02062556592021359,120.41628593220491,98.62751622705991,8.0,4,3,1,18,1607.5,35.1,33.8,0.8072552447552448,0.3008875223205248
+TABPFNV2 (tuned),14614.639715819583,4.849548537201351,3877.419600731425,2.254088269272259,0.4562783530638654,0.48513499648162556,0.08179742986099706,0.05007438478688923,72438.91792383758,165.31166707386078,10.326923076923077,0.1868,8173.816975453165,0.8871031337314181,3445.602606086448,0.9952991538273057,0.34109768962419973,0.46901679172287375,0.05804204088839793,0.02327176321840322,43032.16406688419,48.819771718398094,6.0,1,8,1,16,1588.5,31.1,41.1,0.7880244755244755,0.26951399319966235
+REALMLP (tuned + ensemble),17809.543566583365,5.343982117807764,5995.031437071161,5.920653730744878,0.5983187605703805,0.5877560111308218,0.10395608954668528,0.05250154829922825,112219.73072981052,290.95193637081104,10.865384615384615,0.17424,10344.939689996507,4.694504770967695,5786.691668992368,4.267078452745884,0.5696681015084324,0.5635752491354304,0.051850568481074444,0.04632297855309826,87871.6182808383,267.1363813998378,9.0,0,0,3,23,1572.9,29.0,36.1,0.7757867132867133,0.1417961559305308
+AutoGluon 1.3 (4h),5931.009838958377,3.1656774007357082,3673.8246676849526,3.0250106370782723,0.5599854226187498,0.5522297755522069,0.09461405000435809,0.05278876561410328,39712.28933278582,151.42916243848958,11.884615384615385,0.17691,5020.2655313505065,2.664059625731574,2309.213100138395,2.5543334551748154,0.5600808158121042,0.565709489217623,0.04762778419021996,0.04116173129591805,35357.34871646478,128.98489015193604,8.5,1,2,0,23,1544.1,33.4,44.0,0.7526223776223776,0.17858871441651328
+TABPFNV2 (default),11.884418644151115,1.1739113624279316,5.514520630749067,0.6490305586450037,0.5013642769155608,0.5669313754038009,0.09512311589648129,0.06286359741165812,76.29505964660768,41.758850754001486,12.038461538461538,0.1886,8.093020015292698,0.44081105126274955,4.062007578219401,0.4356807523460313,0.3771347187714357,0.5238664820190138,0.05018180969629027,0.021765272352146064,58.62232186481185,25.39697585954705,5.0,4,1,4,17,1541.6,37.1,37.8,0.7491258741258742,0.2935579164181083
+GBM (tuned + ensemble),1379.7589400635827,3.041326452829899,860.8328230742596,2.952036385308545,0.6806482110513491,0.6837279086393804,0.1176313686476465,0.061906173521871284,10455.248725429481,156.11534656534064,12.903846153846153,0.174085,1321.8940714677174,2.4085231754514904,647.5566852470606,1.7204157994123173,0.7005535661954161,0.7073670918352166,0.051567085599247775,0.05140473915859429,9480.825611975059,103.35126359750211,12.5,1,0,2,23,1521.0,39.2,34.8,0.729458041958042,0.14042554762228465
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+RF (default),0.9640304686676744,0.07426402477117686,0.5084719578868067,0.0844146006139,0.9951894125278877,0.9701912114787967,0.25755513106395067,0.2479832267773854,6.6754175478812146,4.153857105918795,35.28846153846154,0.213105,0.8910642200046115,0.05825435982810126,0.447836563000179,0.06594795127086661,1.0,1.0,0.17586669886220047,0.11692775501298655,5.654732996519019,3.502947097147378,37.5,0,0,0,26,1000.0,0.0,0.0,0.22071678321678323,0.029920050824686344
+LR (default),4.707631549162742,0.1483236100938585,2.804518891637732,0.1437661673750307,0.9745259999639975,0.9520629989740693,0.305476365274656,0.29332097764816845,36.73103788870433,7.308777223189359,35.67307692307692,0.22103499999999998,4.746849238872528,0.08430976470311483,2.2657084486770183,0.10642460584640503,1.0,1.0,0.22930816077470617,0.22370793213879017,31.50980214431158,4.703818974402271,39.5,0,0,1,25,982.3,41.6,38.5,0.21197552447552448,0.03995938942367327
+XT (default),0.8462119445841536,0.0803421718442542,0.47456795510527094,0.08795126020679146,0.9851460583683391,0.9642266228854909,0.2753142737967973,0.27671727525790224,6.0304600748852675,4.406716139948246,36.65384615384615,0.215405,0.7635703219307794,0.06673479080200195,0.40395335630596185,0.07007418015238884,1.0,1.0,0.18424442460299273,0.1562338348801046,5.148113375558061,3.7424721126192138,39.0,0,0,0,26,955.5,43.9,50.8,0.1896853146853147,0.030378669430214802
+KNN (tuned + ensemble),16.440937072497146,0.2739423143558013,7.036183733958932,0.19964653999651732,1.0,0.9945521006475282,0.47993514547383553,0.5914832997832359,57.836601086323085,11.285407568076662,41.57692307692308,0.31118999999999997,6.799899099932777,0.1030390567249722,3.707274221912526,0.16730320161416595,1.0,1.0,0.4552264030449861,0.6183873456591064,55.06974575171796,8.266636963631449,43.0,0,0,0,26,703.5,49.4,45.5,0.0777972027972028,0.024279443329967162
+KNN (tuned),16.440937072497146,0.06075953357240074,7.036183733958932,0.04304950843110991,1.0,0.9966557577121117,0.5010874288021712,0.6455640735444151,57.836601086323085,2.5027008595459077,42.69230769230769,0.31467999999999996,6.799899099932777,0.036990099483066134,3.707274221912526,0.039402452723266784,1.0,1.0,0.4955223463378593,0.6729856958219811,55.06974575171796,2.091430487051362,44.0,0,0,0,26,609.2,52.7,48.0,0.05244755244755245,0.023502270498812147
+KNN (default),0.2899092884145231,0.0317453768518236,0.13426353967394633,0.031065835648244968,1.0,1.0,0.5923454279012129,0.9491257378249622,1.00065410372272,1.4527699655062962,44.25,0.34582,0.1258228341738383,0.02117468251122369,0.07457399441809831,0.021006283652748647,1.0,1.0,0.6201400300158697,1.0,1.0,1.0057335151210618,45.0,0,0,0,26,407.8,86.4,95.3,0.017045454545454544,0.022663376691509682
diff --git a/data/tabpfn-tabicl-imputed/tuning-impact-elo-horizontal.pdf b/data/tabpfn-tabicl-imputed/tuning-impact-elo-horizontal.pdf
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diff --git a/data/tabpfn-tabicl-imputed/tuning-impact-elo-horizontal.png.zip b/data/tabpfn-tabicl-imputed/tuning-impact-elo-horizontal.png.zip
new file mode 100644
index 0000000000000000000000000000000000000000..4f6908fba6c41ba3576608b66bc23a0647bbfbc2
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+size 139645
diff --git a/data/tabpfn-tabicl/figures/critical-diagram.pdf b/data/tabpfn-tabicl/figures/critical-diagram.pdf
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+version https://git-lfs.github.com/spec/v1
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diff --git a/data/tabpfn-tabicl/leaderboard.tex b/data/tabpfn-tabicl/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..b41ff65b5428b5f7a450717fe825914a05f0b501
--- /dev/null
+++ b/data/tabpfn-tabicl/leaderboard.tex
@@ -0,0 +1,53 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+TabPFNv2 (T+E) & \textcolor{gold}{\textbf{1736${}_{-45,+46}$}} & \textcolor{gold}{\textbf{0.700}} & \textcolor{gold}{\textbf{5.8}} & \textcolor{gold}{\textbf{2.2}} & \textcolor{gold}{\textbf{8}} & \textcolor{gold}{\textbf{4.6\%}} & 3445.60 & 48.24 \\
+TabM (T+E) & \textcolor{silver}{\textbf{1612${}_{-37,+35}$}} & 0.483 & \textcolor{silver}{\textbf{9.3}} & 4.5 & 2 & 9.2\% & 2828.45 & 1.60 \\
+TabICL (D) & \textcolor{bronze}{\textbf{1608${}_{-34,+36}$}} & \textcolor{bronze}{\textbf{0.543}} & \textcolor{bronze}{\textbf{9.5}} & \textcolor{silver}{\textbf{3.3}} & \textcolor{silver}{\textbf{4}} & \textcolor{silver}{\textbf{7.1\%}} & 8.89 & 1.74 \\
+TabPFNv2 (T) & 1588${}_{-42,+32}$ & \textcolor{silver}{\textbf{0.544}} & 10.3 & 3.7 & 1 & \textcolor{bronze}{\textbf{8.2\%}} & 3445.60 & 1.00 \\
+RealMLP (T+E) & 1573${}_{-37,+29}$ & 0.402 & 10.9 & 7.1 & 0 & 10.4\% & 5786.69 & 4.27 \\
+AutoGluon 1.3 (4h) & 1544${}_{-44,+34}$ & 0.440 & 11.9 & 5.6 & 1 & 9.5\% & 2309.21 & 2.55 \\
+TabPFNv2 (D) & 1542${}_{-38,+38}$ & 0.499 & 12.0 & \textcolor{bronze}{\textbf{3.4}} & \textcolor{silver}{\textbf{4}} & 9.5\% & 4.06 & 0.44 \\
+LightGBM (T+E) & 1521${}_{-35,+40}$ & 0.319 & 12.9 & 7.1 & 1 & 11.8\% & 647.56 & 1.72 \\
+TabM (T) & 1512${}_{-40,+30}$ & 0.387 & 13.3 & 7.5 & 0 & 10.4\% & 2828.45 & 0.22 \\
+LightGBM (T) & 1462${}_{-34,+33}$ & 0.262 & 15.3 & 13.3 & 0 & 12.2\% & 647.56 & 0.28 \\
+CatBoost (T+E) & 1456${}_{-32,+38}$ & 0.271 & 15.8 & 11.6 & 0 & 11.7\% & 1465.86 & 0.69 \\
+CatBoost (T) & 1435${}_{-32,+36}$ & 0.247 & 16.5 & 10.5 & 0 & 12.0\% & 1465.86 & 0.09 \\
+TabM (D) & 1429${}_{-32,+35}$ & 0.288 & 17.0 & 11.2 & 0 & 13.7\% & 10.42 & 0.15 \\
+ModernNCA (T) & 1426${}_{-30,+37}$ & 0.255 & 17.1 & 8.7 & 1 & 11.7\% & 5944.88 & 0.52 \\
+CatBoost (D) & 1425${}_{-35,+32}$ & 0.228 & 17.0 & 10.0 & 0 & 13.3\% & 5.72 & 0.11 \\
+XGBoost (T+E) & 1415${}_{-33,+36}$ & 0.222 & 17.5 & 14.0 & 0 & 13.1\% & 766.06 & 1.92 \\
+EBM (T+E) & 1383${}_{-33,+31}$ & 0.186 & 19.1 & 12.5 & 0 & 15.7\% & 1109.06 & 0.23 \\
+XGBoost (T) & 1379${}_{-31,+39}$ & 0.181 & 19.2 & 16.8 & 0 & 13.4\% & 766.06 & 0.28 \\
+RealMLP (T) & 1379${}_{-42,+36}$ & 0.201 & 19.3 & 14.8 & 0 & 13.4\% & 5786.69 & 0.25 \\
+ModernNCA (T+E) & 1375${}_{-38,+35}$ & 0.307 & 19.5 & 8.6 & 0 & 12.7\% & 5944.88 & 8.40 \\
+ModernNCA (D) & 1367${}_{-33,+31}$ & 0.183 & 19.8 & 10.0 & 1 & 15.1\% & 14.80 & 0.34 \\
+TorchMLP (T+E) & 1356${}_{-32,+33}$ & 0.199 & 20.3 & 15.0 & 0 & 13.3\% & 2862.05 & 2.16 \\
+FastaiMLP (T+E) & 1356${}_{-35,+32}$ & 0.230 & 20.2 & 11.2 & 0 & 15.7\% & 1358.63 & 8.07 \\
+TabDPT (D) & 1323${}_{-33,+24}$ & 0.258 & 22.0 & 6.1 & 2 & 14.8\% & 27.49 & 8.86 \\
+EBM (T) & 1320${}_{-41,+33}$ & 0.120 & 22.1 & 17.3 & 0 & 16.5\% & 1109.06 & 0.03 \\
+EBM (D) & 1299${}_{-35,+37}$ & 0.153 & 23.0 & 9.7 & 1 & 17.3\% & 5.28 & 0.08 \\
+FastaiMLP (T) & 1269${}_{-40,+38}$ & 0.113 & 24.5 & 19.1 & 0 & 17.2\% & 1358.63 & 0.90 \\
+ExtraTrees (T+E) & 1260${}_{-47,+27}$ & 0.102 & 25.0 & 17.4 & 0 & 18.2\% & 370.85 & 1.47 \\
+TorchMLP (T) & 1238${}_{-33,+33}$ & 0.086 & 25.8 & 22.0 & 0 & 15.9\% & 2862.05 & 0.15 \\
+RealMLP (D) & 1234${}_{-36,+37}$ & 0.083 & 26.1 & 21.8 & 0 & 15.9\% & 36.24 & 0.28 \\
+XGBoost (D) & 1213${}_{-32,+41}$ & 0.032 & 27.1 & 24.8 & 0 & 16.7\% & 2.40 & 0.22 \\
+ExtraTrees (T) & 1200${}_{-32,+35}$ & 0.072 & 27.5 & 22.6 & 0 & 19.4\% & 370.85 & 0.16 \\
+RandomForest (T+E) & 1157${}_{-30,+36}$ & 0.057 & 29.5 & 22.0 & 0 & 19.5\% & 527.42 & 1.39 \\
+LightGBM (D) & 1154${}_{-29,+39}$ & 0.030 & 29.6 & 27.7 & 0 & 17.7\% & 2.90 & 0.13 \\
+RandomForest (T) & 1096${}_{-31,+36}$ & 0.023 & 31.9 & 29.1 & 0 & 20.8\% & 527.42 & 0.12 \\
+TorchMLP (D) & 1082${}_{-34,+36}$ & 0.030 & 32.3 & 28.8 & 0 & 21.4\% & 10.38 & 0.19 \\
+FastaiMLP (D) & 1051${}_{-33,+31}$ & 0.019 & 33.6 & 31.0 & 0 & 23.0\% & 4.73 & 0.62 \\
+Linear (T+E) & 1030${}_{-41,+39}$ & 0.055 & 34.2 & 22.9 & 0 & 28.3\% & 88.63 & 0.26 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.005 & 35.3 & 33.4 & 0 & 25.8\% & 0.45 & 0.07 \\
+Linear (T) & 997${}_{-41,+37}$ & 0.035 & 35.2 & 28.4 & 0 & 29.0\% & 88.63 & 0.09 \\
+Linear (D) & 982${}_{-39,+42}$ & 0.025 & 35.7 & 25.0 & 0 & 30.5\% & 2.27 & 0.11 \\
+ExtraTrees (D) & 956${}_{-51,+44}$ & 0.015 & 36.7 & 32.9 & 0 & 27.5\% & 0.40 & 0.07 \\
+KNN (T+E) & 704${}_{-46,+50}$ & 0.000 & 41.6 & 41.2 & 0 & 48.0\% & 3.71 & 0.17 \\
+KNN (T) & 609${}_{-48,+53}$ & 0.000 & 42.7 & 42.5 & 0 & 50.1\% & 3.71 & 0.04 \\
+KNN (D) & 408${}_{-96,+87}$ & 0.000 & 44.2 & 44.1 & 0 & 59.2\% & 0.07 & 0.02 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/tabpfn-tabicl/tabarena_leaderboard.csv b/data/tabpfn-tabicl/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9cfacb0044514fd9fcb6b0fd592ac532d061c310
--- /dev/null
+++ b/data/tabpfn-tabicl/tabarena_leaderboard.csv
@@ -0,0 +1,46 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+TABPFNV2 (tuned + ensemble),14614.639715819583,148.11643430860633,3877.419600731425,65.38306906011915,0.2999799624009034,0.36592302109392316,0.046199591967015556,0.028953147052208875,72438.91792383758,5032.936436246271,5.788461538461538,0.17261500000000002,8173.816975453165,60.12680508957969,3445.602606086448,48.23597351862546,0.21441671773452015,0.4068697696081146,0.021640889295981347,0.016874625134937742,43032.16406688419,2931.913108083719,3.5,8,4,1,13,1736.3,45.7,44.6,0.8911713286713286,0.46269305993659526
+TABM (tuned + ensemble),7080.034926002046,2.290320276704609,3436.5218404284533,2.1079046445931953,0.5166525238887076,0.547775172494987,0.09238776741660952,0.04985850997225548,43123.835967101855,114.22499982092596,9.307692307692308,0.17379499999999998,6405.6722231189415,1.880999751885732,2828.4486752645407,1.5957955528425738,0.47136645123500964,0.5540981099375837,0.04617144277378826,0.034316541138176816,44088.87353980956,109.46959390379024,8.0,2,1,1,22,1612.5,34.1,36.5,0.8111888111888111,0.22027167779430223
+TABICL (default),21.933824618351764,2.7954679915028757,10.278805574502684,2.035731958506663,0.45732829959408833,0.5181742657898207,0.07108757868512468,0.03468928052253071,145.15967621212897,119.47187491598106,9.48076923076923,0.17207,19.54939921696981,2.1857474181387158,8.891901835466445,1.7433667301105085,0.46959481282562443,0.583210155619012,0.03487584587193143,0.02062556592021359,120.41628593220491,98.62751622705991,8.0,4,3,1,18,1607.5,35.1,33.8,0.8072552447552448,0.3008875223205248
+TABPFNV2 (tuned),14614.639715819583,4.849548537201351,3877.419600731425,2.254088269272259,0.4562783530638654,0.48513499648162556,0.08179742986099706,0.05007438478688923,72438.91792383758,165.31166707386078,10.326923076923077,0.1868,8173.816975453165,0.8871031337314181,3445.602606086448,0.9952991538273057,0.34109768962419973,0.46901679172287375,0.05804204088839793,0.02327176321840322,43032.16406688419,48.819771718398094,6.0,1,8,1,16,1588.5,31.1,41.1,0.7880244755244755,0.26951399319966235
+REALMLP (tuned + ensemble),17809.543566583365,5.343982117807764,5995.031437071161,5.920653730744878,0.5983187605703805,0.5877560111308218,0.10395608954668528,0.05250154829922825,112219.73072981052,290.95193637081104,10.865384615384615,0.17424,10344.939689996507,4.694504770967695,5786.691668992368,4.267078452745884,0.5696681015084324,0.5635752491354304,0.051850568481074444,0.04632297855309826,87871.6182808383,267.1363813998378,9.0,0,0,3,23,1572.9,29.0,36.1,0.7757867132867133,0.1417961559305308
+AutoGluon 1.3 (4h),5931.009838958377,3.1656774007357082,3673.8246676849526,3.0250106370782723,0.5599854226187498,0.5522297755522069,0.09461405000435809,0.05278876561410328,39712.28933278582,151.42916243848958,11.884615384615385,0.17691,5020.2655313505065,2.664059625731574,2309.213100138395,2.5543334551748154,0.5600808158121042,0.565709489217623,0.04762778419021996,0.04116173129591805,35357.34871646478,128.98489015193604,8.5,1,2,0,23,1544.1,33.4,44.0,0.7526223776223776,0.17858871441651328
+TABPFNV2 (default),11.884418644151115,1.1739113624279316,5.514520630749067,0.6490305586450037,0.5013642769155608,0.5669313754038009,0.09512311589648129,0.06286359741165812,76.29505964660768,41.758850754001486,12.038461538461538,0.1886,8.093020015292698,0.44081105126274955,4.062007578219401,0.4356807523460313,0.3771347187714357,0.5238664820190138,0.05018180969629027,0.021765272352146064,58.62232186481185,25.39697585954705,5.0,4,1,4,17,1541.6,37.1,37.8,0.7491258741258742,0.2935579164181083
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+XT (tuned + ensemble),714.8477392964893,1.554570463987497,476.1600651525857,1.7304383155841179,0.8984673764067701,0.8650357556797086,0.18213060995030939,0.12358064556047872,6037.128774148354,86.31947081771706,25.0,0.18718,684.9222148127026,1.3082488920953539,370.85408017752667,1.4664534567412004,1.0,0.927972542444979,0.09673248729372463,0.07594781768304604,5339.627074654447,77.0823275943278,28.25,0,0,0,26,1260.2,26.7,47.0,0.45454545454545453,0.057367362828272636
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+LR (tuned + ensemble),175.85719627702338,0.42670365847074065,115.79203038641191,0.374819481777715,0.9447815704031849,0.9377863465231412,0.28317417183852894,0.24822823084079834,1448.7019616321465,20.41052886468638,34.23076923076923,0.216585,158.39292872746785,0.19311302105585734,88.63237206036187,0.25697546561814155,1.0,1.0,0.21769005088471027,0.1779543124949825,1246.7830478036524,13.25530093456624,38.0,0,0,1,25,1030.4,38.5,40.3,0.24475524475524477,0.04368314033358164
+LR (tuned),175.85719627702338,0.1364539579448537,115.79203038641191,0.12123112010680681,0.9645430999513104,0.943877751101422,0.2902155463327537,0.25773756332102926,1448.7019616321465,6.009867470278231,35.21153846153846,0.21681,158.39292872746785,0.07476819356282552,88.63237206036187,0.08838151119168902,1.0,1.0,0.22518752798442315,0.1834788883245299,1246.7830478036524,4.016028876859062,38.0,0,0,0,26,996.9,36.4,40.6,0.22246503496503497,0.03516566041445386
+RF (default),0.9640304686676744,0.07426402477117686,0.5084719578868067,0.0844146006139,0.9951894125278877,0.9701912114787967,0.25755513106395067,0.2479832267773854,6.6754175478812146,4.153857105918795,35.28846153846154,0.213105,0.8910642200046115,0.05825435982810126,0.447836563000179,0.06594795127086661,1.0,1.0,0.17586669886220047,0.11692775501298655,5.654732996519019,3.502947097147378,37.5,0,0,0,26,1000.0,0.0,0.0,0.22071678321678323,0.029920050824686344
+LR (default),4.707631549162742,0.1483236100938585,2.804518891637732,0.1437661673750307,0.9745259999639975,0.9520629989740693,0.305476365274656,0.29332097764816845,36.73103788870433,7.308777223189359,35.67307692307692,0.22103499999999998,4.746849238872528,0.08430976470311483,2.2657084486770183,0.10642460584640503,1.0,1.0,0.22930816077470617,0.22370793213879017,31.50980214431158,4.703818974402271,39.5,0,0,1,25,982.3,41.6,38.5,0.21197552447552448,0.03995938942367327
+XT (default),0.8462119445841536,0.0803421718442542,0.47456795510527094,0.08795126020679146,0.9851460583683391,0.9642266228854909,0.2753142737967973,0.27671727525790224,6.0304600748852675,4.406716139948246,36.65384615384615,0.215405,0.7635703219307794,0.06673479080200195,0.40395335630596185,0.07007418015238884,1.0,1.0,0.18424442460299273,0.1562338348801046,5.148113375558061,3.7424721126192138,39.0,0,0,0,26,955.5,43.9,50.8,0.1896853146853147,0.030378669430214802
+KNN (tuned + ensemble),16.440937072497146,0.2739423143558013,7.036183733958932,0.19964653999651732,1.0,0.9945521006475282,0.47993514547383553,0.5914832997832359,57.836601086323085,11.285407568076662,41.57692307692308,0.31118999999999997,6.799899099932777,0.1030390567249722,3.707274221912526,0.16730320161416595,1.0,1.0,0.4552264030449861,0.6183873456591064,55.06974575171796,8.266636963631449,43.0,0,0,0,26,703.5,49.4,45.5,0.0777972027972028,0.024279443329967162
+KNN (tuned),16.440937072497146,0.06075953357240074,7.036183733958932,0.04304950843110991,1.0,0.9966557577121117,0.5010874288021712,0.6455640735444151,57.836601086323085,2.5027008595459077,42.69230769230769,0.31467999999999996,6.799899099932777,0.036990099483066134,3.707274221912526,0.039402452723266784,1.0,1.0,0.4955223463378593,0.6729856958219811,55.06974575171796,2.091430487051362,44.0,0,0,0,26,609.2,52.7,48.0,0.05244755244755245,0.023502270498812147
+KNN (default),0.2899092884145231,0.0317453768518236,0.13426353967394633,0.031065835648244968,1.0,1.0,0.5923454279012129,0.9491257378249622,1.00065410372272,1.4527699655062962,44.25,0.34582,0.1258228341738383,0.02117468251122369,0.07457399441809831,0.021006283652748647,1.0,1.0,0.6201400300158697,1.0,1.0,1.0057335151210618,45.0,0,0,0,26,407.8,86.4,95.3,0.017045454545454544,0.022663376691509682
diff --git a/data/tabpfn-tabicl/tuning-impact-elo-horizontal.pdf b/data/tabpfn-tabicl/tuning-impact-elo-horizontal.pdf
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diff --git a/data/tabpfn-tabicl/tuning-impact-elo-horizontal.png.zip b/data/tabpfn-tabicl/tuning-impact-elo-horizontal.png.zip
new file mode 100644
index 0000000000000000000000000000000000000000..4f6908fba6c41ba3576608b66bc23a0647bbfbc2
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+version https://git-lfs.github.com/spec/v1
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+size 139645
diff --git a/data/tabpfn/figures/critical-diagram.pdf b/data/tabpfn/figures/critical-diagram.pdf
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+version https://git-lfs.github.com/spec/v1
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diff --git a/data/tabpfn/leaderboard.tex b/data/tabpfn/leaderboard.tex
new file mode 100644
index 0000000000000000000000000000000000000000..c807b083ac0087135393b2136efc4b5a8cdbf43a
--- /dev/null
+++ b/data/tabpfn/leaderboard.tex
@@ -0,0 +1,52 @@
+\begin{tabular}{llcccccrr}
+\toprule
+\textbf{Model} & \textbf{Elo ($\uparrow$)} & \textbf{Norm.} & \textbf{Avg.} & \textbf{Harm.} & \textbf{\#wins ($\uparrow$)} & \textbf{Improva-} & \textbf{Train time} & \textbf{Predict time} \\
+ &  & \textbf{score ($\uparrow$)} & \textbf{rank ($\downarrow$)} & \textbf{mean} &  & \textbf{bility ($\downarrow$)} & \textbf{per 1K [s]} & \textbf{per 1K [s]} \\
+ &  &  &  & \textbf{rank ($\downarrow$)} &  &  &  &  \\
+\midrule
+TabPFNv2 (T+E) & \textcolor{gold}{\textbf{1722${}_{-34,+44}$}} & \textcolor{gold}{\textbf{0.712}} & \textcolor{gold}{\textbf{5.5}} & \textcolor{gold}{\textbf{1.9}} & \textcolor{gold}{\textbf{12}} & \textcolor{gold}{\textbf{3.4\%}} & 3899.42 & 55.83 \\
+RealMLP (T+E) & \textcolor{silver}{\textbf{1590${}_{-34,+33}$}} & 0.477 & \textcolor{silver}{\textbf{9.3}} & 5.7 & 0 & 8.1\% & 6131.85 & 4.53 \\
+TabM (T+E) & \textcolor{bronze}{\textbf{1586${}_{-32,+31}$}} & 0.484 & \textcolor{bronze}{\textbf{9.5}} & 4.6 & 2 & \textcolor{bronze}{\textbf{7.5\%}} & 3372.56 & 1.66 \\
+AutoGluon 1.3 (4h) & 1565${}_{-39,+30}$ & \textcolor{bronze}{\textbf{0.493}} & 10.2 & 4.3 & 3 & 7.7\% & 2727.51 & 3.24 \\
+TabPFNv2 (T) & 1564${}_{-30,+28}$ & \textcolor{silver}{\textbf{0.540}} & 10.4 & \textcolor{bronze}{\textbf{3.7}} & 1 & \textcolor{silver}{\textbf{6.6\%}} & 3899.42 & 0.98 \\
+LightGBM (T+E) & 1522${}_{-35,+33}$ & 0.361 & 11.8 & 7.1 & 1 & 10.0\% & 771.57 & 2.49 \\
+TabPFNv2 (D) & 1499${}_{-28,+33}$ & 0.483 & 12.7 & \textcolor{silver}{\textbf{3.5}} & \textcolor{silver}{\textbf{5}} & 8.3\% & 4.22 & 0.55 \\
+CatBoost (T+E) & 1483${}_{-32,+29}$ & 0.342 & 13.5 & 9.9 & 0 & 9.7\% & 2034.85 & 0.80 \\
+TabM (T) & 1470${}_{-26,+32}$ & 0.382 & 13.8 & 8.0 & 0 & 8.7\% & 3372.56 & 0.21 \\
+CatBoost (T) & 1462${}_{-34,+36}$ & 0.316 & 14.4 & 9.2 & 0 & 9.9\% & 2034.85 & 0.10 \\
+LightGBM (T) & 1452${}_{-34,+31}$ & 0.288 & 14.8 & 12.9 & 0 & 10.5\% & 771.57 & 0.32 \\
+XGBoost (T+E) & 1426${}_{-30,+29}$ & 0.271 & 15.8 & 12.9 & 0 & 11.1\% & 828.74 & 2.31 \\
+CatBoost (D) & 1422${}_{-29,+31}$ & 0.259 & 16.2 & 8.8 & 1 & 11.5\% & 8.51 & 0.12 \\
+ModernNCA (T) & 1401${}_{-36,+29}$ & 0.228 & 17.1 & 9.3 & 1 & 10.6\% & 6147.69 & 0.48 \\
+TabM (D) & 1396${}_{-35,+33}$ & 0.295 & 17.2 & 11.6 & 0 & 11.6\% & 12.24 & 0.15 \\
+XGBoost (T) & 1393${}_{-31,+27}$ & 0.229 & 17.4 & 15.4 & 0 & 11.4\% & 828.74 & 0.34 \\
+ModernNCA (T+E) & 1391${}_{-41,+26}$ & 0.320 & 17.5 & 7.8 & 0 & 11.0\% & 6147.69 & 8.15 \\
+TabDPT (D) & 1380${}_{-41,+30}$ & 0.370 & 18.1 & 4.0 & \textcolor{silver}{\textbf{5}} & 11.9\% & 28.84 & 9.01 \\
+RealMLP (T) & 1365${}_{-28,+35}$ & 0.221 & 18.6 & 14.4 & 0 & 11.1\% & 6131.85 & 0.26 \\
+ModernNCA (D) & 1344${}_{-39,+28}$ & 0.157 & 19.8 & 10.8 & 1 & 13.7\% & 16.16 & 0.31 \\
+EBM (T+E) & 1341${}_{-30,+31}$ & 0.186 & 19.8 & 11.4 & 0 & 14.8\% & 1331.68 & 0.21 \\
+TorchMLP (T+E) & 1331${}_{-32,+29}$ & 0.186 & 20.3 & 15.0 & 0 & 12.0\% & 3704.30 & 2.07 \\
+FastaiMLP (T+E) & 1303${}_{-33,+35}$ & 0.181 & 21.6 & 12.5 & 0 & 14.6\% & 1459.62 & 8.06 \\
+EBM (T) & 1284${}_{-29,+33}$ & 0.127 & 22.5 & 15.7 & 0 & 15.5\% & 1331.68 & 0.02 \\
+ExtraTrees (T+E) & 1273${}_{-34,+26}$ & 0.131 & 23.0 & 14.5 & 0 & 16.3\% & 416.39 & 1.39 \\
+EBM (D) & 1256${}_{-37,+34}$ & 0.136 & 23.7 & 11.0 & 1 & 16.3\% & 5.89 & 0.07 \\
+RealMLP (D) & 1249${}_{-31,+23}$ & 0.099 & 24.2 & 20.0 & 0 & 13.6\% & 37.06 & 0.31 \\
+TorchMLP (T) & 1234${}_{-25,+33}$ & 0.097 & 24.8 & 21.0 & 0 & 14.1\% & 3704.30 & 0.14 \\
+ExtraTrees (T) & 1228${}_{-35,+27}$ & 0.102 & 25.2 & 19.1 & 0 & 17.3\% & 416.39 & 0.18 \\
+FastaiMLP (T) & 1222${}_{-32,+27}$ & 0.089 & 25.3 & 20.3 & 0 & 15.9\% & 1459.62 & 0.89 \\
+XGBoost (D) & 1199${}_{-29,+30}$ & 0.025 & 26.4 & 24.4 & 0 & 15.0\% & 3.05 & 0.24 \\
+LightGBM (D) & 1170${}_{-34,+32}$ & 0.027 & 27.6 & 26.0 & 0 & 15.5\% & 3.39 & 0.16 \\
+RandomForest (T+E) & 1168${}_{-34,+33}$ & 0.071 & 27.7 & 21.0 & 0 & 17.7\% & 572.67 & 1.42 \\
+RandomForest (T) & 1112${}_{-33,+28}$ & 0.036 & 30.0 & 27.0 & 0 & 18.8\% & 572.67 & 0.14 \\
+TorchMLP (D) & 1068${}_{-33,+27}$ & 0.026 & 31.6 & 28.5 & 0 & 19.2\% & 11.82 & 0.15 \\
+FastaiMLP (D) & 1026${}_{-30,+25}$ & 0.015 & 33.1 & 31.0 & 0 & 21.7\% & 5.18 & 0.65 \\
+ExtraTrees (D) & 1007${}_{-32,+31}$ & 0.030 & 33.7 & 29.5 & 0 & 24.3\% & 0.42 & 0.08 \\
+RandomForest (D) & 1000${}_{-0,+0}$ & 0.004 & 33.9 & 32.1 & 0 & 23.2\% & 0.47 & 0.07 \\
+Linear (T+E) & 972${}_{-37,+28}$ & 0.044 & 34.7 & 24.5 & 0 & 29.3\% & 97.00 & 0.22 \\
+Linear (T) & 943${}_{-39,+36}$ & 0.028 & 35.6 & 28.9 & 0 & 29.9\% & 97.00 & 0.09 \\
+Linear (D) & 922${}_{-35,+36}$ & 0.020 & 36.1 & 26.8 & 0 & 31.4\% & 2.99 & 0.10 \\
+KNN (T+E) & 697${}_{-55,+41}$ & 0.000 & 40.5 & 40.2 & 0 & 45.7\% & 3.41 & 0.18 \\
+KNN (T) & 603${}_{-51,+57}$ & 0.000 & 41.6 & 41.5 & 0 & 47.5\% & 3.41 & 0.04 \\
+KNN (D) & 373${}_{-68,+68}$ & 0.000 & 43.3 & 43.2 & 0 & 55.6\% & 0.07 & 0.03 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/data/tabpfn/tabarena_leaderboard.csv b/data/tabpfn/tabarena_leaderboard.csv
new file mode 100644
index 0000000000000000000000000000000000000000..6bfbe8ecf04e21a712e55035ac60cc20bd3e7df1
--- /dev/null
+++ b/data/tabpfn/tabarena_leaderboard.csv
@@ -0,0 +1,45 @@
+method,time_train_s,time_infer_s,time_train_s_per_1K,time_infer_s_per_1K,normalized-error,normalized-error-task,champ_delta,loss_rescaled,time_train_s_rescaled,time_infer_s_rescaled,rank,median_metric_error,median_time_train_s,median_time_infer_s,median_time_train_s_per_1K,median_time_infer_s_per_1K,median_normalized-error,median_normalized-error-task,median_champ_delta,median_loss_rescaled,median_time_train_s_rescaled,median_time_infer_s_rescaled,median_rank,rank=1_count,rank=2_count,rank=3_count,rank>3_count,elo,elo+,elo-,winrate,mrr
+TABPFNV2 (tuned + ensemble),14425.017745771953,147.33083187377815,4401.396029095723,73.66242671660264,0.28759504252950974,0.347282565892165,0.033791532255816104,0.021691804403474596,97206.53847125142,5748.512821917254,5.53030303030303,0.22151,6554.713698530197,48.78112569650014,3899.4164285155534,55.8334682198027,0.16748308582785468,0.3569968688242239,0.004663083714453231,0.0038383724774552082,76922.9404189982,3038.197671565286,2.0,12,6,2,13,1722.1,43.1,34.0,0.8946441155743481,0.5154452222510298
+REALMLP (tuned + ensemble),17640.05257734851,4.903230364073808,6337.298676302214,5.714285721500293,0.5227695654892056,0.5205835812389092,0.08113802091931344,0.04132085084573313,135567.2437894661,291.2042190402348,9.303030303030303,0.23449,10127.618620618185,4.333946492936876,6131.852125259443,4.526544125425011,0.48215030896347355,0.5296754767960605,0.03823555615847496,0.029161376498297843,109144.7404232934,273.1739755067715,8.0,0,2,3,28,1590.4,32.7,33.3,0.806906272022551,0.17535539684098442
+TABM (tuned + ensemble),7759.403633462299,2.1001035646156025,4063.8948551919407,2.0262676290994976,0.5158505640872573,0.527488621478328,0.07468532292643498,0.05142130142823952,68994.91604679843,112.78737728828092,9.454545454545455,0.22996,6619.307261109352,1.565106577343411,3372.559747313985,1.6579582265448953,0.47020083976979304,0.539573473894149,0.0321092955544684,0.027567784172117953,47614.825535817596,107.26839105814781,8.0,2,2,3,26,1586.2,30.2,31.3,0.8033826638477801,0.21855177068820775
+AutoGluon 1.3 (4h),6055.818895130286,4.270678644549565,3922.7285559191146,3.7248760797186677,0.5066593479144164,0.5016434556313996,0.07722981074251968,0.046889638099060306,55071.75177189554,226.07763766376502,10.242424242424242,0.23252,5245.15874774456,2.643711381488376,2727.5131652494383,3.239980099911348,0.4597995521540763,0.5024076802392037,0.031736497159312704,0.025571480573160055,42990.839716832954,133.93997628187762,7.0,3,2,1,27,1565.1,29.4,38.8,0.7850599013389711,0.2313227448922636
+TABPFNV2 (tuned),14425.017745771953,5.209287579613502,4401.396029095723,2.5426283197700723,0.46046523778783577,0.4854802409226216,0.06647673195285816,0.048875587042492535,97206.53847125142,204.9255427422306,10.378787878787879,0.22735,6554.713698530197,0.8280300378799439,3899.4164285155534,0.9826616035428936,0.34176855083335494,0.4778694665116624,0.03437211181910249,0.021856445988952963,76922.9404189982,51.81666634218519,6.0,1,11,1,20,1563.8,27.4,29.4,0.7818886539816773,0.26853674831779045
+GBM (tuned + ensemble),1415.9841766067627,4.799208115006135,910.5198965732433,4.069284327482318,0.6392387450549837,0.6388440602683771,0.09997972856333269,0.058027135210861426,13668.933422648086,267.51058203721857,11.833333333333334,0.22857,1305.722465435664,2.759434594048394,771.5692555555095,2.4904788987789197,0.6693216689945842,0.6259564939415712,0.04177838790149224,0.03428335611022812,13185.517460723013,113.47473834277966,11.0,1,0,2,30,1521.9,32.8,34.1,0.748062015503876,0.1410994418891387
+TABPFNV2 (default),11.52095359335042,1.0633954972129076,6.06940244179637,0.672221391108446,0.5171946106727229,0.5695628859334582,0.08250610447990993,0.06704584581218105,98.45527177302218,43.21321073170591,12.696969696969697,0.22839,8.920613225301107,0.4424108028411865,4.216795518748306,0.554313340790968,0.3939035196311629,0.49361879391754265,0.043516491196731844,0.023742158338286197,86.18639524525906,32.62493249187355,5.0,5,0,6,22,1499.0,32.4,28.0,0.7279774489076815,0.28415420757209386
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+XT (default),0.822556706068893,0.08127484409897415,0.471496074166279,0.09148027851361618,0.9701664672021088,0.947954236878204,0.2425079699137419,0.2454783416017478,7.127294712723843,4.867665484175484,33.72727272727273,0.2625,0.7219058142768012,0.06215476195017497,0.4238388518146615,0.07849177235630946,1.0,1.0,0.1794327271141959,0.15384123117868195,6.235138739070136,4.456198846093199,37.0,0,0,0,33,1007.1,30.6,31.3,0.23890063424947147,0.033934460671698106
+RF (default),1.0802604700981167,0.07677059518769132,0.52542853627611,0.08878926590315098,0.9962098401734872,0.9695037757004908,0.23203683579868908,0.23508878524732915,8.967503792674067,4.676640526479693,33.86363636363637,0.25251,0.7427172581354777,0.058771981133355036,0.4717373991313263,0.06943642688143947,1.0,1.0,0.16087354235509543,0.1389292060353615,7.464730143591374,4.060095192168379,35.0,0,0,0,33,1000.0,0.0,0.0,0.2357293868921776,0.031175635120658556
+LR (tuned + ensemble),170.87050911529295,0.355976880680431,121.02102704404008,0.32634615259593897,0.9564945706206911,0.9495867900946459,0.2932871848480949,0.32561555425887745,1772.9723454272128,18.055698085268993,34.72727272727273,0.24491,158.05876021915012,0.1903169314066569,96.99876412252586,0.21682568555752907,1.0,1.0,0.21751004569734023,0.24054270012702494,1402.8748923675635,12.175213508812917,39.0,0,0,1,32,972.3,27.9,36.9,0.2156448202959831,0.04087606611291446
+LR (tuned),170.87050911529295,0.11554940750301887,121.02102704404008,0.10905393298119077,0.972064260567699,0.9545798343796298,0.29890337785985527,0.3331774019669149,1772.9723454272128,5.509546013888674,35.621212121212125,0.25255,158.05876021915012,0.058829413519965276,96.99876412252586,0.0862505760249892,1.0,1.0,0.2329968805680923,0.2575122541077485,1402.8748923675635,4.067640041721783,39.5,0,0,0,33,943.4,35.8,38.3,0.19485553206483439,0.03454590417164617
+LR (default),4.536491992417409,0.12851071365754613,2.8968102616210682,0.12996863092563715,0.9799295757292101,0.9610495592933687,0.3140075680375095,0.37538468089996596,44.19552977352603,6.787762182097637,36.13636363636363,0.26248,3.583410120010376,0.07990590731302898,2.994106463982051,0.104980896680783,1.0,1.0,0.26184876537499124,0.29385559917332205,34.79737790723628,4.682238532824547,39.5,0,0,1,32,921.5,35.2,34.5,0.1828752642706131,0.03726832355599832
+KNN (tuned + ensemble),13.854036316245494,0.24958616836303815,6.0685782814510345,0.19751524231739334,1.0,0.9936006831500522,0.4566757533990582,0.6138711800871106,58.80470682969773,11.4223913906534,40.54545454545455,0.31971,5.889678266313341,0.10710635185241699,3.411499284183487,0.17676389939136214,1.0,1.0,0.41352429770943944,0.6488391163428452,55.92066201498072,9.83646280007513,42.0,0,0,0,33,697.1,40.7,54.9,0.080338266384778,0.024871015659946344
+KNN (tuned),13.854036316245494,0.05394502376466488,6.0685782814510345,0.040282320435423104,1.0,0.9969136397430987,0.4748160330502654,0.665617927340854,58.80470682969773,2.390983322122401,41.60606060606061,0.34215,5.889678266313341,0.029062509536743164,3.411499284183487,0.03624739765555662,1.0,1.0,0.4641563796701971,0.740394718900499,55.92066201498072,2.000488003155834,43.0,0,0,0,33,603.2,56.1,50.1,0.05567300916138125,0.024112613850887754
+KNN (default),0.24162639913334188,0.029173843387000086,0.11473320891232033,0.030260209027344123,1.0,0.9999519518442885,0.555978281969172,0.946826696237823,1.0005153544482035,1.4920918647664636,43.31818181818182,0.40639,0.11169254779815674,0.019644896189371746,0.06555315548394994,0.02595407415488982,1.0,1.0,0.5396030287721358,1.0,1.0,1.2186499153225778,44.0,0,0,0,33,373.1,67.6,67.9,0.015856236786469344,0.023142914628718914
diff --git a/data/tabpfn/tuning-impact-elo-horizontal.pdf b/data/tabpfn/tuning-impact-elo-horizontal.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..1b43f99538ce393e70bc09f8078cc31ba867c1af
Binary files /dev/null and b/data/tabpfn/tuning-impact-elo-horizontal.pdf differ
diff --git a/data/tabpfn/tuning-impact-elo-horizontal.png.zip b/data/tabpfn/tuning-impact-elo-horizontal.png.zip
new file mode 100644
index 0000000000000000000000000000000000000000..0082e1650b4a3bd25e03e26f768da4c4f07b5581
--- /dev/null
+++ b/data/tabpfn/tuning-impact-elo-horizontal.png.zip
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:96f37ca0e8626cc2055e6be64880740be0f6b8950004c69fc545431190ec8d16
+size 135563
diff --git a/data_pdfs_to_pngs.py b/data_pdfs_to_pngs.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ffc71aec50f3f62080fa4bbb56e013010dcc1da
--- /dev/null
+++ b/data_pdfs_to_pngs.py
@@ -0,0 +1,23 @@
+"""Helper script to go from PDF to PNGs we can use in HTML on the LB."""
+
+from __future__ import annotations
+
+import glob
+import os
+from pathlib import Path
+
+from pdf2image import convert_from_path
+
+root_dir = "./data"
+pdf_paths = glob.glob(os.path.join(root_dir, "**", "*.pdf"), recursive=True)
+
+
+for pdf_path in pdf_paths:
+    # Relative path to recreate folder structure
+    path_to_pdf = Path(pdf_path).resolve()
+    path_to_png = path_to_pdf.with_suffix(".png.zip")
+    print(f"Converting {pdf_path}...")
+
+    images = convert_from_path(pdf_path, dpi=800)
+    for _i, image in enumerate(images):
+        image.save(path_to_png, "PNG")
diff --git a/main.py b/main.py
index 93ae02c6a9a2fbb413287701514548a5b926a15a..e6b1529aa30b2363c0a7f21c22695780ff0470e9 100644
--- a/main.py
+++ b/main.py
@@ -1,124 +1,22 @@
 from __future__ import annotations
 
+from dataclasses import dataclass
 from pathlib import Path
 
 import gradio as gr
 import pandas as pd
+import website_texts
 from apscheduler.schedulers.background import BackgroundScheduler
 from constants import Constants, model_type_emoji
 from gradio_leaderboard import ColumnFilter, Leaderboard, SelectColumns
-
-TITLE = """<h1 align="center" id="space-title">TabArena Leaderboard for Predictive Machine Learning on IID Tabular Data</h1>"""
-
-INTRODUCTION_TEXT = """
-TabArena is a living benchmark system for predictive machine learning on tabular data.
-The goal of TabArena and its leaderboard is to asses the peak performance of
-model-specific pipelines.
-
-**Datasets:** Currently, the leaderboard is based on a manually curated collection of
-51 tabular classification and regression datasets for independent and identically distributed
-(IID) data, spanning the small to medium data regime. The datasets were carefully
-curated to represent various real-world predictive machine learning use cases.
-
-**Models:** The focus of the leaderboard is on model-specific pipelines. Each pipeline
-is evaluated with default or tuned hyperparameter configuration or as an ensemble of
-tuned configurations. Each model is implemented in a tested real-world pipeline that was
-optimized to get the most out of the model by the maintainers of TabArena, and where
-possible together with the authors of the model.
-
-**Metrics:** The leaderboard is ranked based on Elo. We present several additional
-metrics. See the `About` tab for more information on the metrics.
-
-**Reference Pipeline:** The leaderboard includes a reference pipeline, which is applied
-independently of the tuning protocol and constraints we constructed for models within TabArena.
-The reference pipeline aims to represent the performance quickly achievable by a
-practitioner on a dataset. The current reference pipeline is the predictive machine
-learning system AutoGluon (version 1.3, with the best_quality preset and
-4 hours for training). AutoGluon represents an ensemble pipeline across various model
-types and thus provides a reference for model-specific pipelines.
-
-The current leaderboard is based on TabArena-v0.1.
-"""
-
-
-ABOUT_TEXT = """
-TabArena is a living benchmark system for predictive machine learning on tabular data.
-We introduce TabArena and provide an overview of TabArena-v0.1 in our paper: TBA.
-
-## Using TabArena for Benchmarking
-To compare your own methods to the pre-computed results for all models on the leaderboard,
-you can use the TabArena framework. For examples on how to use TabArena for benchmarking,
-please see https://github.com/TabArena/tabarena_benchmarking_examples
-
-## Contributing to the Leaderboard; Contributing Models
-For guidelines on how to contribute your model to TabArena, or the result of your model
-to the official leaderboard, please see the appendix of our paper: TBA. 
-
-## Contributing Data
-For anything related to the datasets used in TabArena, please see https://github.com/TabArena/tabarena_dataset_curation
-
----
-
-## Leaderboard Documentation
-
-The leaderboard is ranked by Elo and includes several other metrics. Here is a short 
-description for these metrics: 
-
-#### Elo
-We evaluate models using the Elo rating system, following Chatbot Arena. Elo is a 
-pairwise comparison-based rating system where each model's rating predicts its expected
-win probability against others, with a 400-point Elo gap corresponding to a 10 to 1 
-(91\%) expected win rate. We calibrate 1000 Elo to the performance of our default 
-random forest configuration across all figures, and perform 100 rounds of bootstrapping 
-to obtain 95\% confidence intervals. Elo scores are computed using ROC AUC for binary
-classification, log-loss for multiclass classification, and RMSE for regression. 
-
-#### Normalized Score
-Following TabRepo, we linearly rescale the error such that the best method has a 
-normalized score of one, and the median method has a normalized score of 0. Scores 
-below zero are clipped to zero. These scores are then averaged across datasets.
-
-#### Average Rank
-Ranks of methods are computed on each dataset (lower is better) and averaged.
-
-#### Harmonic Mean Rank
-Taking the harmonic mean of ranks, 1/((1/N) * sum(1/rank_i for i in range(N))), 
-more strongly favors methods having very low ranks on some datasets. It therefore favors
- methods that are sometimes very good and sometimes very bad over methods that are 
- always mediocre, as the former are more likely to be useful in conjunction with 
- other methods.
- 
-#### Improvability
-We introduce improvability as a metric that measures how many percent lower the error 
-of the best method is than the current method on a dataset. This is then averaged over 
-datasets. Formally, for a single dataset improvability is (err_i - besterr_i)/err_i * 100\%.
-Improvability is always between 0\% and 100\%.   
-
----
-
-## Contact 
-
-For most inquires, please open issues in the relevant GitHub repository or here on
-HuggingFace. 
-
-For any other inquiries related to TabArena, please reach out to: contact@tabarena.ai
-
-### Core Maintainers
-The current core maintainers of TabArena are:
-[Nick Erickson](https://github.com/Innixma), 
-[Lennart Purucker](https://github.com/LennartPurucker/), 
-[Andrej Tschalzev](https://github.com/atschalz), 
-[David Holzmüller](https://github.com/dholzmueller)
-"""
-
-CITATION_BUTTON_LABEL = (
-    "If you use TabArena or the leaderboard in your research please cite the following:"
+from website_texts import (
+    ABOUT_TEXT,
+    CITATION_BUTTON_LABEL,
+    CITATION_BUTTON_TEXT,
+    INTRODUCTION_TEXT,
+    TITLE,
+    VERSION_HISTORY_BUTTON_TEXT,
 )
-CITATION_BUTTON_TEXT = r"""
-@article{
-    TBA,
-}
-"""
 
 
 def get_model_family(model_name: str) -> str:
@@ -164,14 +62,10 @@ def rename_map(model_name: str) -> str:
     return model_name
 
 
-def load_data(filename: str):
-    df_leaderboard = pd.read_csv(Path(__file__).parent / "data" / f"{filename}.csv.zip")
-    print(
-        f"Loaded dataframe with {len(df_leaderboard)} rows and columns {df_leaderboard.columns}"
-    )
-
-    # add model family information
+def load_data(filename: str, data_source="data"):
+    df_leaderboard = pd.read_csv(Path(__file__).parent / data_source / filename)
 
+    # Add Model Family Information
     df_leaderboard["Type"] = df_leaderboard.loc[:, "method"].apply(
         lambda s: model_type_emoji[get_model_family(s)]
     )
@@ -189,7 +83,7 @@ def load_data(filename: str):
     )
     # select only the columns we want to display
     df_leaderboard["normalized-score"] = 1 - df_leaderboard["normalized-error"]
-    df_leaderboard["hmr"] = 1/df_leaderboard["mrr"]
+    df_leaderboard["hmr"] = 1 / df_leaderboard["mrr"]
     df_leaderboard["improvability"] = 100 * df_leaderboard["champ_delta"]
     df_leaderboard = df_leaderboard.loc[
         :,
@@ -215,7 +109,9 @@ def load_data(filename: str):
     df_leaderboard[["median_time_train_s_per_1K", "rank", "hmr"]] = df_leaderboard[
         ["median_time_train_s_per_1K", "rank", "hmr"]
     ].round(2)
-    df_leaderboard[["normalized-score", "median_time_infer_s_per_1K", "improvability"]] = df_leaderboard[
+    df_leaderboard[
+        ["normalized-score", "median_time_infer_s_per_1K", "improvability"]
+    ] = df_leaderboard[
         ["normalized-score", "median_time_infer_s_per_1K", "improvability"]
     ].round(3)
 
@@ -237,39 +133,149 @@ def load_data(filename: str):
         }
     )
 
+
+@dataclass
+class LBContainer:
+    name: str
+    file_name: str
+    blurb: str
+    overview_image_name: str | None
+    df_leaderboard: pd.DataFrame | None = None
+    imputed_map: dict[str, float] | None = None
+
+    def __post_init__(self):
+        self.df_leaderboard = load_data(self.file_name)
+        self.df_leaderboard = _add_imputed_logic(
+            self.df_leaderboard, imputed_map=self.imputed_map
+        )
+
+
+def make_overview_image(
+    overview_image_name: str | None, data_source: str = "data"
+) -> None:
+    # No Image to display
+    path_to_image = str(Path(__file__).parent / data_source / overview_image_name)
+    gr.Image(path_to_image, label="Leaderboard Overview", show_label=True, height=550)
+
+
+def make_overview_leaderboard(lbs: [LBContainer]):
+    # Create column per LB
+    all_models = {
+        m.split("[")[0].strip()
+        for lb in lbs
+        for m in lb.df_leaderboard[
+            ~lb.df_leaderboard["TypeName"].isin(["Reference Pipeline"])
+        ]["Model"]
+        .unique()
+        .tolist()
+    }
+
+    full_df = None
+    for lb in lbs:
+        df = lb.df_leaderboard.copy()
+        df = df[~df["TypeName"].isin(["Reference Pipeline"])]
+        df[lb.name] = df["Elo [⬆️]"].rank(ascending=False, method="first").astype(int)
+        df = df.sort_values(by=lb.name, ascending=True)
+
+        # Adding indicators does not work as it makes it a string and then not sort
+        #   correctly.
+        # df[lb.name] = df[lb.name].astype(str)
+        # df[lb.name] = df[lb.name].replace({
+        #     "1": "🥇 1",
+        #     "2": "🥈 2",
+        #     "3": "🥉 3",
+        #     }
+        # )
+
+        df = df[["Type", "Model", lb.name]]
+        # Remove imputed message.
+        df["Model"] = (
+            df["Model"].apply(lambda x: x.split("[")[0].strip()).astype("string")
+        )
+
+        if full_df is None:
+            # TODO: add support in case a model did not run on the full LB.
+            assert all_models.difference(set(df["Model"].unique())) == set()
+            full_df = df
+        else:
+            df = df[["Model", lb.name]]
+            df_models = set(df["Model"].unique())
+            missing_models = all_models.difference(df_models)
+            if missing_models:
+                missing_models_df = pd.DataFrame(
+                    [[mm, len(df_models) + 1] for mm in missing_models],
+                    columns=["Model", lb.name],
+                )
+                df = pd.concat([df, missing_models_df], ignore_index=True)
+            df["Model"] = df["Model"].astype("string")
+            # Merge
+            full_df = full_df.merge(df, how="left", on="Model", validate="1:1")
+
+    medal_colors = ["#665600", "#4D4D4D", "#523314"]  # bronze is usually a hex value
+
+    # Highlight function
+    def highlight_top3(col):
+        styles = [""] * len(col)
+        for index_i in range(len(col)):
+            if col.iloc[index_i] <= 3:
+                styles[index_i] = (
+                    f"background-color: {medal_colors[col.iloc[index_i] - 1]};"
+                )
+
+        return styles
+
+    styler = full_df.style.apply(highlight_top3, axis=0, subset=[lb.name for lb in lbs])
+
+    return gr.DataFrame(
+        styler,
+        pinned_columns=2,
+        interactive=False,
+        show_search="search",
+        label="The ranking of all models across various leaderboards.",
+    )
+
+
 def make_leaderboard(df_leaderboard: pd.DataFrame) -> Leaderboard:
+    # -- Add filters
     df_leaderboard["TypeFiler"] = df_leaderboard["TypeName"].apply(
         lambda m: f"{m} {model_type_emoji[m]}"
     )
-    # De-selects but does not filter...
-    # default = df_leaderboard["TypeFiler"].unique().tolist()
-    # default = [(s, s) for s in default if "AutoML" not in s]
-
     df_leaderboard["Only Default"] = df_leaderboard["Model"].str.endswith("(default)")
     df_leaderboard["Only Tuned"] = df_leaderboard["Model"].str.endswith("(tuned)")
     df_leaderboard["Only Tuned + Ensemble"] = df_leaderboard["Model"].str.endswith(
         "(tuned + ensemble)"
     ) | df_leaderboard["Model"].str.endswith("(4h)")
 
-    # Add Imputed count postfix
-    mask = df_leaderboard["Model"].str.startswith("TabPFNv2")
-    df_leaderboard.loc[mask, "Model"] = (
-        df_leaderboard.loc[mask, "Model"] + " [35.29% IMPUTED]"
-    )
-    mask = df_leaderboard["Model"].str.startswith("TabICL")
-    df_leaderboard.loc[mask, "Model"] = (
-        df_leaderboard.loc[mask, "Model"] + " [29.41% IMPUTED]"
-    )
+    filter_columns = [
+        ColumnFilter("TypeFiler", type="checkboxgroup", label="🤖 Model Types"),
+        ColumnFilter("Only Default", type="boolean", default=False),
+        ColumnFilter("Only Tuned", type="boolean", default=False),
+        ColumnFilter("Only Tuned + Ensemble", type="boolean", default=False),
+    ]
 
-    df_leaderboard["Imputed"] = df_leaderboard["Model"].str.startswith(
-        "TabPFNv2"
-    ) | df_leaderboard["Model"].str.startswith("TabICL")
-    df_leaderboard["Imputed"] = df_leaderboard["Imputed"].replace(
-        {
-            True: "Imputed",
-            False: "Not Imputed",
-        }
-    )
+    # Add Imputed count postfix
+    if any(df_leaderboard["Imputed"]):
+        df_leaderboard["Imputed"] = df_leaderboard["Imputed"].replace(
+            {
+                True: "Imputed",
+                False: "Not Imputed",
+            }
+        )
+        filter_columns.append(
+            ColumnFilter(
+                "Imputed",
+                type="checkboxgroup",
+                label="(Not) Imputed Models",
+                info="We impute the performance for models that cannot run on all"
+                " datasets due to task or dataset size constraints (e.g. TabPFN,"
+                " TabICL). We impute with the performance of a default RandomForest."
+                " We add a postfix [X% IMPUTED] to the model if any results were"
+                " imputed. The X% shows the percentage of"
+                " datasets that were imputed. In general, imputation negatively"
+                " represents the model performance, punishing the model for not"
+                " being able to run on all datasets.",
+            )
+        )
 
     return Leaderboard(
         value=df_leaderboard,
@@ -287,73 +293,190 @@ def make_leaderboard(df_leaderboard: pd.DataFrame) -> Leaderboard:
             "Only Tuned + Ensemble",
             "Imputed",
         ],
-        search_columns=["Model", "Type"],
-        filter_columns=[
-            ColumnFilter("TypeFiler", type="checkboxgroup", label="Model Types."),
-            ColumnFilter("Only Default", type="boolean", default=False),
-            ColumnFilter("Only Tuned", type="boolean", default=False),
-            ColumnFilter("Only Tuned + Ensemble", type="boolean", default=False),
-            ColumnFilter(
-                "Imputed",
-                type="checkboxgroup",
-                label="(Not) Imputed Models.",
-                info="We impute the performance for models that cannot run on all"
-                " datasets due to task or dataset size constraints (e.g. TabPFN,"
-                " TabICL). We impute with the performance of a default RandomForest."
-                " We add a postfix [X% IMPUTED] to the model if any results were"
-                " imputed. The X% shows the percentage of"
-                " datasets that were imputed. In general, imputation negatively"
-                " represents the model performance, punishing the model for not"
-                " being able to run on all datasets.",
-            ),
-        ],
+        search_columns=["Model", "TypeName"],
+        filter_columns=filter_columns,
         bool_checkboxgroup_label="Custom Views (exclusive, only toggle one at a time):",
+        height=800,
+    )
+
+
+# TODO: get this from LB instead of manually adding it.
+def _add_imputed_logic(df_leaderboard, imputed_map: dict[str, float] | None) -> None:
+    df_leaderboard = df_leaderboard.copy()
+    if imputed_map is not None:
+        for model_name, imputed_percentage in imputed_map.items():
+            if imputed_percentage == 100:
+                # Filter methods that are fully imputed.
+                df_leaderboard = df_leaderboard[
+                    ~df_leaderboard["Model"].str.startswith(model_name)
+                ]
+            else:
+                mask = df_leaderboard["Model"].str.startswith(model_name)
+                df_leaderboard.loc[mask, "Model"] = (
+                    df_leaderboard.loc[mask, "Model"]
+                    + f" [{imputed_percentage:.2f}% IMPUTED]"
+                )
+                df_leaderboard.loc[mask, "Imputed"] = True
+    else:
+        df_leaderboard["Imputed"] = False
+    return df_leaderboard
+
+
+def _get_lbs() -> tuple[LBContainer, ...]:
+    ta = LBContainer(
+        name="🏅 TabArena",
+        file_name="full-imputed/tabarena_leaderboard.csv",
+        overview_image_name="full-imputed/tuning-impact-elo.png.zip",
+        blurb="Leaderboard for all datasets including all (imputed) models.",
+        imputed_map={
+            "TabPFNv2": 35.29,
+            "TabICL": 29.41,
+        },
+    )
+    ta_lite = LBContainer(
+        name="TabArena-Lite",
+        # FIXME: get full-imputed based LB
+        file_name="lite/full/tabarena_leaderboard.csv",
+        overview_image_name="lite/full/tuning-impact-elo.png.zip",
+        blurb="Leaderboard for one split (1st fold, 1st repeat) for all datasets including all (imputed) models.",
+        imputed_map=None,
     )
+    ta_clf = LBContainer(
+        name="TabArena-Classification",
+        file_name="full-imputed-cls/tabarena_leaderboard.csv",
+        overview_image_name="full-imputed-cls/tuning-impact-elo.png.zip",
+        blurb="Leaderboard for all 38 classification datasets including all (imputed) models.",
+        imputed_map={
+            "TabPFNv2": 31.58,
+            "TabICL": 5.26,
+        },
+    )
+    ta_reg = LBContainer(
+        name="TabArena-Regression",
+        file_name="full-imputed-reg/tabarena_leaderboard.csv",
+        # FIXME: get overview image without TabICL
+        overview_image_name="full-imputed-reg/tuning-impact-elo.png.zip",
+        blurb="Leaderboard for all 13 regression datasets including all (imputed) models.",
+        imputed_map={
+            "TabPFNv2": 46.15,
+            "TabICL": 100,
+        },
+    )
+    ta_tabicl = LBContainer(
+        name="⚡ TabArena (TabICL-compatible)",
+        file_name="tabicl-imputed/tabarena_leaderboard.csv",
+        overview_image_name="tabicl-imputed/tuning-impact-elo-horizontal.png.zip",
+        blurb="Leaderboard for all 36 datasets within the constraints of TabICL including all (imputed) models.",
+        imputed_map={
+            "TabPFNv2": 27.78,
+        },
+    )
+    ta_tabpfn = LBContainer(
+        name="⚡ TabArena (TabPFN-compatible)",
+        file_name="tabpfn-imputed/tabarena_leaderboard.csv",
+        overview_image_name="tabpfn-imputed/tuning-impact-elo-horizontal.png.zip",
+        blurb="Leaderboard for all 33 datasets within the constraints of TabPFN including all (imputed) models.",
+        imputed_map={
+            "TabICL": 21.21,
+        },
+    )
+    ta_tabpfn_tabicl = LBContainer(
+        name="TabArena (TabPFN-TabICL-compatible)",
+        file_name="tabpfn-tabicl/tabarena_leaderboard.csv",
+        overview_image_name="tabpfn-tabicl/tuning-impact-elo-horizontal.png.zip",
+        blurb="Leaderboard for all 26 datasets within the constraints of TabPFN and TabICL including all models.",
+    )
+
+    return ta, ta_lite, ta_clf, ta_reg, ta_tabicl, ta_tabpfn, ta_tabpfn_tabicl
 
 
 def main():
-    demo = gr.Blocks()
+    css = """
+    .markdown-text-box {
+        padding: 4px;
+        border-radius: 2px;
+    }
+    """
+    demo = gr.Blocks(css=css, title="TabArena")
     with demo:
         gr.HTML(TITLE)
-        gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
 
-        with gr.Tabs(elem_classes="tab-buttons"):
-            with gr.TabItem("🏅 TabArena-v0.1", elem_id="llm-benchmark-tab-table", id=2):
-                df_leaderboard = load_data("tabarena_leaderboard")
-                make_leaderboard(df_leaderboard)
-
-            # TODO: decide on which subsets we want to support here.
-            # with gr.TabItem('🏅 Regression', elem_id="llm-benchmark-tab-table", id=0):
-            #     df_leaderboard = load_data("leaderboard-regression")
-            #     leaderboard = make_leaderboard(df_leaderboard)
-            #
-            # with gr.TabItem('🏅 Classification', elem_id="llm-benchmark-tab-table", id=1):
-            #     df_leaderboard = load_data("leaderboard-classification")
-            #     leaderboard = make_leaderboard(df_leaderboard)
-            #
-            # with gr.TabItem('🏅 Classification', elem_id="llm-benchmark-tab-table", id=1):
-            #     df_leaderboard = load_data("leaderboard-classification")
-            #     leaderboard = make_leaderboard(df_leaderboard)
-            #
-            # with gr.TabItem('🏅 TabPFNv2-Compatible', elem_id="llm-benchmark-tab-table", id=1):
-            #     df_leaderboard = load_data("leaderboard-classification")
-            #     leaderboard = make_leaderboard(df_leaderboard)
-            #
-            # with gr.TabItem('🏅 TabICL-Compatible', elem_id="llm-benchmark-tab-table", id=1):
-            #     df_leaderboard = load_data("leaderboard-classification")
-            #     leaderboard = make_leaderboard(df_leaderboard)
-
-            with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=4):
-                gr.Markdown(ABOUT_TEXT, elem_classes="markdown-text")
+        # -- Introduction
+        gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+        with gr.Row():
+            with gr.Column(), gr.Accordion("📊 Datasets", open=False):
+                gr.Markdown(
+                    website_texts.OVERVIEW_DATASETS, elem_classes="markdown-text-box"
+                )
+
+            with gr.Column(), gr.Accordion("🤖 Models", open=False):
+                gr.Markdown(
+                    website_texts.OVERVIEW_MODELS, elem_classes="markdown-text-box"
+                )
+        with gr.Row():
+            with gr.Column(), gr.Accordion("📈 Metrics", open=False):
+                gr.Markdown(
+                    website_texts.OVERVIEW_METRICS, elem_classes="markdown-text-box"
+                )
+            with gr.Column(), gr.Accordion("📊 Reference Pipeline", open=False):
+                gr.Markdown(
+                    website_texts.OVERVIEW_REF_PIPE, elem_classes="markdown-text-box"
+                )
+        with gr.Row(), gr.Accordion("📝 More Details", open=False):
+            gr.Markdown(ABOUT_TEXT, elem_classes="markdown-text-box")
         with gr.Row(), gr.Accordion("📙 Citation", open=False):
             gr.Textbox(
                 value=CITATION_BUTTON_TEXT,
                 label=CITATION_BUTTON_LABEL,
-                lines=20,
+                lines=5,
                 elem_id="citation-button",
                 show_copy_button=True,
             )
 
+        # -- Get all LBs we need:
+        ta, ta_lite, ta_clf, ta_reg, ta_tabicl, ta_tabpfn, ta_tabpfn_tabicl = _get_lbs()
+
+        # -- LB Overview
+        gr.Markdown("## 🗺️ Overview")
+        ordered_lbs = [
+            ta,
+            ta_clf,
+            ta_reg,
+            ta_tabicl,
+            ta_tabpfn,
+            ta_tabpfn_tabicl,
+            ta_lite,
+        ]
+        make_overview_leaderboard(lbs=ordered_lbs)
+
+        gr.Markdown("## 🏆 Leaderboards")
+        with gr.Tabs(elem_classes="tab-buttons"):
+            for lb_id, lb in enumerate(ordered_lbs):
+                with gr.TabItem(lb.name, elem_id="llm-benchmark-tab-table", id=lb_id):
+                    gr.Markdown(lb.blurb, elem_classes="markdown-text")
+                    make_overview_image(lb.overview_image_name)
+                    make_leaderboard(lb.df_leaderboard)
+
+        with gr.Row(), gr.Accordion("📂 Version History", open=False):
+            gr.Markdown(VERSION_HISTORY_BUTTON_TEXT, elem_classes="markdown-text")
+
+            gr.Markdown("## Old Leaderboards")
+            with (
+                gr.Tabs(elem_classes="tab-buttons"),
+                gr.TabItem("TabArena-v0.1", elem_id="llm-benchmark-tab-table", id=2),
+            ):
+                df_leaderboard = load_data(
+                    "tabarena_leaderboard.csv.zip", data_source="old_data"
+                )
+                df_leaderboard = _add_imputed_logic(
+                    df_leaderboard,
+                    imputed_map={
+                        "TabPFNv2": 35.29,
+                        "TabICL": 29.41,
+                    },
+                )
+                make_leaderboard(df_leaderboard)
+
     scheduler = BackgroundScheduler()
     # scheduler.add_job(restart_space, "interval", seconds=1800)
     scheduler.start()
diff --git a/data/tabarena_leaderboard.csv.zip b/old_data/tabarena_leaderboard.csv.zip
similarity index 100%
rename from data/tabarena_leaderboard.csv.zip
rename to old_data/tabarena_leaderboard.csv.zip
diff --git a/pyproject.toml b/pyproject.toml
index 617d0c0e81dfcc138c22facd42a643b4c3e63695..ace5887eed625ad6847009eae07bc6c4c69837ac 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,13 +1,17 @@
 [project]
 name = "tabarenaleaderboard"
-version = "0.1.0"
+version = "0.1.1"
 description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
     "apscheduler>=3.11.0",
     "gradio-client>=1.3.0",
-    "gradio-leaderboard==0.0.9",
-    "gradio[oauth]==4.44.0",
+    "gradio-leaderboard==0.0.13",
+    "gradio[oauth]==5.33.2",
     "pandas>=2.2.3",
+    "pdf2image"
 ]
+[project.optional-dependencies]
+dev = ["pdf2image"]
+
diff --git a/requirements.txt b/requirements.txt
index e4bd8fa6b1ec2c8bf7321d996d5c21059f0d7b57..8800ed8753adb52c9a6860de78ee7672279ccaa9 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,7 +1,6 @@
 APScheduler
-gradio==4.44.0
-gradio[oauth]==4.44.0
-gradio_leaderboard==0.0.9
+gradio==5.33.2
+gradio_leaderboard==0.0.13
 gradio_client
 pandas
 python-dateutil
\ No newline at end of file
diff --git a/website_texts.py b/website_texts.py
new file mode 100644
index 0000000000000000000000000000000000000000..54e48638ec9669eeb1aa2937e4654b7b36cb2ae0
--- /dev/null
+++ b/website_texts.py
@@ -0,0 +1,130 @@
+from __future__ import annotations
+
+TITLE = """<h1 align="center" id="space-title">TabArena Leaderboard for Predictive Machine Learning on IID Tabular Data</h1>"""
+INTRODUCTION_TEXT = """
+TabArena is a living benchmark system for predictive machine learning on tabular data.
+The goal of TabArena and its leaderboard is to asses the peak performance of
+model-specific pipelines.
+"""
+
+OVERVIEW_DATASETS = """
+The leaderboard is based on a manually curated collection of
+51 tabular classification and regression datasets for independent and identically distributed
+(IID) data, spanning the small to medium data regime. The datasets were carefully
+curated to represent various real-world predictive machine learning use cases.
+"""
+OVERVIEW_MODELS = """
+The focus of the leaderboard is on model-specific pipelines. Each pipeline
+is evaluated with default and tuned hyperparameter configuration or as an ensemble of
+tuned configurations. Each model is implemented in a tested real-world pipeline that was
+optimized to get the most out of the model by the maintainers of TabArena, and where
+possible together with the authors of the model.
+"""
+OVERVIEW_METRICS = """
+The leaderboards are ranked based on Elo. We present several additional
+metrics. See `More Details` for more information on the metrics.
+
+**Note, we impute** the performance for models that cannot run on all datasets due to
+task or dataset size constraints (e.g. TabPFN, TabICL). In general, imputation
+negatively represents the model performance, punishing the model for not being able
+to run on all datasets. We provide leaderboards computed only on the subset of datasets
+where TabPFN, TabICL, or both can run. We denote these leaderboards by adding a
+`X-compatible` postfix.
+"""
+OVERVIEW_REF_PIPE = """
+The leaderboard includes a reference pipeline, which is applied
+independently of the tuning protocol and constraints we constructed for models within TabArena.
+The reference pipeline aims to represent the performance quickly achievable by a
+practitioner on a dataset. The current reference pipeline is the predictive machine
+learning system AutoGluon (version 1.3, with the best_quality preset and
+4 hours for training). AutoGluon represents an ensemble pipeline across various model
+types and thus provides a reference for model-specific pipelines.
+"""
+
+ABOUT_TEXT = r"""
+### Extended Overview of TabArena (References / Papers)
+We introduce TabArena and provide an overview of TabArena-v0.1 in our paper: TBA.
+
+### Using TabArena for Benchmarking
+To compare your own methods to the pre-computed results for all models on the leaderboard,
+you can use the TabArena framework. For examples on how to use TabArena for benchmarking,
+please see https://tabarena.ai/code-examples
+
+### Contributing to the Leaderboard; Contributing Models
+For guidelines on how to contribute your model to TabArena, or the result of your model
+to the official leaderboard, please see the appendix of our paper: TBA.
+
+### Contributing Data
+For anything related to the datasets used in TabArena, please see https://tabarena.ai/data-tabular-ml-iid-study
+
+---
+
+### Leaderboard Documentation
+
+The leaderboard is ranked by Elo and includes several other metrics. Here is a short
+description for these metrics:
+
+#### Elo
+We evaluate models using the Elo rating system, following Chatbot Arena. Elo is a
+pairwise comparison-based rating system where each model's rating predicts its expected
+win probability against others, with a 400-point Elo gap corresponding to a 10 to 1
+(91\%) expected win rate. We calibrate 1000 Elo to the performance of our default
+random forest configuration across all figures, and perform 100 rounds of bootstrapping
+to obtain 95\% confidence intervals. Elo scores are computed using ROC AUC for binary
+classification, log-loss for multiclass classification, and RMSE for regression.
+
+#### Normalized Score
+Following TabRepo, we linearly rescale the error such that the best method has a
+normalized score of one, and the median method has a normalized score of 0. Scores
+below zero are clipped to zero. These scores are then averaged across datasets.
+
+#### Average Rank
+Ranks of methods are computed on each dataset (lower is better) and averaged.
+
+#### Harmonic Mean Rank
+Taking the harmonic mean of ranks, 1/((1/N) * sum(1/rank_i for i in range(N))),
+more strongly favors methods having very low ranks on some datasets. It therefore favors
+ methods that are sometimes very good and sometimes very bad over methods that are
+ always mediocre, as the former are more likely to be useful in conjunction with
+ other methods.
+
+#### Improvability
+We introduce improvability as a metric that measures how many percent lower the error
+of the best method is than the current method on a dataset. This is then averaged over
+datasets. Formally, for a single dataset improvability is (err_i - besterr_i)/err_i * 100\%.
+Improvability is always between 0\% and 100\%.
+
+---
+
+### Contact
+
+For most inquires, please open issues in the relevant GitHub repository or here on
+HuggingFace.
+
+For any other inquiries related to TabArena, please reach out to: mail@tabarena.ai
+
+### Core Maintainers
+The current core maintainers of TabArena are:
+[Nick Erickson](https://github.com/Innixma),
+[Lennart Purucker](https://github.com/LennartPurucker/),
+[Andrej Tschalzev](https://github.com/atschalz),
+[David Holzmüller](https://github.com/dholzmueller)
+"""
+
+CITATION_BUTTON_LABEL = (
+    "If you use TabArena or the leaderboard in your research please cite the following:"
+)
+CITATION_BUTTON_TEXT = r"""@article{
+    TBA,
+}
+"""
+
+VERSION_HISTORY_BUTTON_TEXT = """
+**Current Version: TabArena-v0.1.1**
+
+The following details updates to the leaderboard (date format is YYYY/MM/DD):
+
+* 2025/06/13: Add data for all subsets and re-runs on GPU; Add leaderboards for subsets;
+ new overview; add Figures to LBs.
+* 2025/05: Initialization of the TabArena-v0.1 leaderboard.
+"""