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Online Variance Reduction with Mixtures
| 14 |
icml
| 1 | 0 |
2023-06-17 03:10:02.470000
|
https://github.com/zalanborsos/variance-reduction-mixtures
| 3 |
Online variance reduction with mixtures
|
https://scholar.google.com/scholar?cluster=14403425847063612414&hl=en&as_sdt=0,10
| 2 | 2,019 |
Compositional Fairness Constraints for Graph Embeddings
| 197 |
icml
| 18 | 2 |
2023-06-17 03:10:02.685000
|
https://github.com/joeybose/Flexible-Fairness-Constraints
| 44 |
Compositional fairness constraints for graph embeddings
|
https://scholar.google.com/scholar?cluster=2983154672519525426&hl=en&as_sdt=0,5
| 4 | 2,019 |
Active Manifolds: A non-linear analogue to Active Subspaces
| 21 |
icml
| 2 | 0 |
2023-06-17 03:10:02.900000
|
https://github.com/bridgesra/active-manifold-icml2019-code
| 4 |
Active Manifolds: A non-linear analogue to Active Subspaces
|
https://scholar.google.com/scholar?cluster=12766453925065005709&hl=en&as_sdt=0,31
| 2 | 2,019 |
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
| 240 |
icml
| 21 | 3 |
2023-06-17 03:10:03.116000
|
https://github.com/hiwonjoon/ICML2019-TREX
| 71 |
Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations
|
https://scholar.google.com/scholar?cluster=14944046691955331663&hl=en&as_sdt=0,10
| 6 | 2,019 |
Understanding the Origins of Bias in Word Embeddings
| 168 |
icml
| 12 | 3 |
2023-06-17 03:10:03.331000
|
https://github.com/mebrunet/understanding-bias
| 21 |
Understanding the origins of bias in word embeddings
|
https://scholar.google.com/scholar?cluster=18061585171680402541&hl=en&as_sdt=0,5
| 3 | 2,019 |
Low Latency Privacy Preserving Inference
| 161 |
icml
| 68 | 0 |
2023-06-17 03:10:03.547000
|
https://github.com/microsoft/CryptoNets
| 242 |
Low latency privacy preserving inference
|
https://scholar.google.com/scholar?cluster=86142108232916247&hl=en&as_sdt=0,5
| 13 | 2,019 |
Active Embedding Search via Noisy Paired Comparisons
| 16 |
icml
| 3 | 0 |
2023-06-17 03:10:03.764000
|
https://github.com/siplab-gt/pairsearch
| 9 |
Active embedding search via noisy paired comparisons
|
https://scholar.google.com/scholar?cluster=10123441327203003064&hl=en&as_sdt=0,23
| 3 | 2,019 |
Dynamic Measurement Scheduling for Event Forecasting using Deep RL
| 12 |
icml
| 6 | 1 |
2023-06-17 03:10:03.978000
|
https://github.com/zzzace2000/autodiagnosis
| 9 |
Dynamic measurement scheduling for event forecasting using deep RL
|
https://scholar.google.com/scholar?cluster=16682086403586827063&hl=en&as_sdt=0,5
| 3 | 2,019 |
Stein Point Markov Chain Monte Carlo
| 55 |
icml
| 4 | 1 |
2023-06-17 03:10:04.193000
|
https://github.com/wilson-ye-chen/sp-mcmc
| 12 |
Stein point markov chain monte carlo
|
https://scholar.google.com/scholar?cluster=6889028915730960186&hl=en&as_sdt=0,33
| 0 | 2,019 |
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
| 165 |
icml
| 30 | 4 |
2023-06-17 03:10:04.408000
|
https://github.com/xinshi-chen/GenerativeAdversarialUserModel
| 124 |
Generative adversarial user model for reinforcement learning based recommendation system
|
https://scholar.google.com/scholar?cluster=18416272509453441398&hl=en&as_sdt=0,5
| 4 | 2,019 |
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
| 291 |
icml
| 23 | 2 |
2023-06-17 03:10:04.622000
|
https://github.com/chenpf1025/noisy_label_understanding_utilizing
| 82 |
Understanding and utilizing deep neural networks trained with noisy labels
|
https://scholar.google.com/scholar?cluster=1459914703144318986&hl=en&as_sdt=0,33
| 6 | 2,019 |
Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation
| 358 |
icml
| 17 | 3 |
2023-06-17 03:10:04.837000
|
https://github.com/thuml/Batch-Spectral-Penalization
| 78 |
Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation
|
https://scholar.google.com/scholar?cluster=8590630247063758749&hl=en&as_sdt=0,5
| 5 | 2,019 |
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications
| 24 |
icml
| 6 | 0 |
2023-06-17 03:10:05.054000
|
https://github.com/pinyuchen/FINGER
| 6 |
Fast incremental von neumann graph entropy computation: Theory, algorithm, and applications
|
https://scholar.google.com/scholar?cluster=15943782295657868941&hl=en&as_sdt=0,5
| 2 | 2,019 |
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
| 7 |
icml
| 1 | 0 |
2023-06-17 03:10:05.269000
|
https://github.com/MintYiqingchen/MMI-ALI
| 5 |
Multivariate-information adversarial ensemble for scalable joint distribution matching
|
https://scholar.google.com/scholar?cluster=2407036909986043494&hl=en&as_sdt=0,5
| 0 | 2,019 |
Robust Decision Trees Against Adversarial Examples
| 105 |
icml
| 11 | 3 |
2023-06-17 03:10:05.483000
|
https://github.com/chenhongge/RobustTrees
| 64 |
Robust decision trees against adversarial examples
|
https://scholar.google.com/scholar?cluster=18298482644739407816&hl=en&as_sdt=0,31
| 8 | 2,019 |
RaFM: Rank-Aware Factorization Machines
| 12 |
icml
| 6 | 1 |
2023-06-17 03:10:05.698000
|
https://github.com/cxsmarkchan/RaFM
| 12 |
RaFM: rank-aware factorization machines
|
https://scholar.google.com/scholar?cluster=9961787920931572726&hl=en&as_sdt=0,11
| 4 | 2,019 |
Control Regularization for Reduced Variance Reinforcement Learning
| 67 |
icml
| 5 | 0 |
2023-06-17 03:10:05.913000
|
https://github.com/rcheng805/CORE-RL
| 30 |
Control regularization for reduced variance reinforcement learning
|
https://scholar.google.com/scholar?cluster=4210711157444974813&hl=en&as_sdt=0,5
| 1 | 2,019 |
Predictor-Corrector Policy Optimization
| 21 |
icml
| 3 | 1 |
2023-06-17 03:10:06.127000
|
https://github.com/gtrll/rlfamily
| 3 |
Predictor-corrector policy optimization
|
https://scholar.google.com/scholar?cluster=13913575152899689436&hl=en&as_sdt=0,31
| 4 | 2,019 |
Neural Joint Source-Channel Coding
| 96 |
icml
| 13 | 2 |
2023-06-17 03:10:06.342000
|
https://github.com/ermongroup/necst
| 38 |
Neural joint source-channel coding
|
https://scholar.google.com/scholar?cluster=13260217163651536800&hl=en&as_sdt=0,5
| 5 | 2,019 |
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
| 54 |
icml
| 14 | 0 |
2023-06-17 03:10:06.556000
|
https://github.com/IBM/online-alt-min
| 22 |
Beyond backprop: Online alternating minimization with auxiliary variables
|
https://scholar.google.com/scholar?cluster=13143560607415133217&hl=en&as_sdt=0,5
| 9 | 2,019 |
MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization
| 161 |
icml
| 52 | 16 |
2023-06-17 03:10:06.771000
|
https://github.com/sosuperic/MeanSum
| 112 |
Meansum: A neural model for unsupervised multi-document abstractive summarization
|
https://scholar.google.com/scholar?cluster=11126017598001925179&hl=en&as_sdt=0,33
| 10 | 2,019 |
Quantifying Generalization in Reinforcement Learning
| 532 |
icml
| 84 | 3 |
2023-06-17 03:10:06.985000
|
https://github.com/openai/coinrun
| 361 |
Quantifying generalization in reinforcement learning
|
https://scholar.google.com/scholar?cluster=9870113474300692969&hl=en&as_sdt=0,26
| 134 | 2,019 |
Certified Adversarial Robustness via Randomized Smoothing
| 1,382 |
icml
| 71 | 3 |
2023-06-17 03:10:07.200000
|
https://github.com/locuslab/smoothing
| 318 |
Certified adversarial robustness via randomized smoothing
|
https://scholar.google.com/scholar?cluster=7039519782328477041&hl=en&as_sdt=0,14
| 11 | 2,019 |
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
| 174 |
icml
| 6 | 1 |
2023-06-17 03:10:07.415000
|
https://github.com/flowersteam/curious
| 26 |
Curious: intrinsically motivated modular multi-goal reinforcement learning
|
https://scholar.google.com/scholar?cluster=329489517258350795&hl=en&as_sdt=0,48
| 12 | 2,019 |
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets
| 17 |
icml
| 1 | 0 |
2023-06-17 03:10:07.629000
|
https://github.com/pjcv/smh
| 3 |
Scalable Metropolis-Hastings for exact Bayesian inference with large datasets
|
https://scholar.google.com/scholar?cluster=10400262915897387298&hl=en&as_sdt=0,33
| 1 | 2,019 |
Minimal Achievable Sufficient Statistic Learning
| 12 |
icml
| 4 | 0 |
2023-06-17 03:10:07.844000
|
https://github.com/mwcvitkovic/MASS-Learning
| 8 |
Minimal achievable sufficient statistic learning
|
https://scholar.google.com/scholar?cluster=16216829176165913924&hl=en&as_sdt=0,33
| 3 | 2,019 |
Open Vocabulary Learning on Source Code with a Graph-Structured Cache
| 42 |
icml
| 10 | 0 |
2023-06-17 03:10:08.060000
|
https://github.com/mwcvitkovic/Deep_Learning_On_Code_With_A_Graph_Vocabulary--Code_Preprocessor
| 21 |
Open vocabulary learning on source code with a graph-structured cache
|
https://scholar.google.com/scholar?cluster=1145489630896909786&hl=en&as_sdt=0,41
| 2 | 2,019 |
Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations
| 76 |
icml
| 27 | 16 |
2023-06-17 03:10:08.277000
|
https://github.com/HazyResearch/butterfly
| 118 |
Learning fast algorithms for linear transforms using butterfly factorizations
|
https://scholar.google.com/scholar?cluster=8670371133727236715&hl=en&as_sdt=0,10
| 20 | 2,019 |
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
| 99 |
icml
| 1 | 0 |
2023-06-17 03:10:08.492000
|
https://github.com/prolearner/onlineLTL
| 3 |
Learning-to-learn stochastic gradient descent with biased regularization
|
https://scholar.google.com/scholar?cluster=5491276692157599761&hl=en&as_sdt=0,5
| 4 | 2,019 |
Sever: A Robust Meta-Algorithm for Stochastic Optimization
| 251 |
icml
| 6 | 0 |
2023-06-17 03:10:08.709000
|
https://github.com/hoonose/sever
| 26 |
Sever: A robust meta-algorithm for stochastic optimization
|
https://scholar.google.com/scholar?cluster=1735563344640957243&hl=en&as_sdt=0,32
| 4 | 2,019 |
Approximated Oracle Filter Pruning for Destructive CNN Width Optimization
| 104 |
icml
| 10 | 2 |
2023-06-17 03:10:08.929000
|
https://github.com/ShawnDing1994/AOFP
| 30 |
Approximated oracle filter pruning for destructive cnn width optimization
|
https://scholar.google.com/scholar?cluster=979238780615518812&hl=en&as_sdt=0,5
| 3 | 2,019 |
Trajectory-Based Off-Policy Deep Reinforcement Learning
| 5 |
icml
| 2 | 0 |
2023-06-17 03:10:09.157000
|
https://github.com/boschresearch/DD_OPG
| 11 |
Trajectory-based off-policy deep reinforcement learning
|
https://scholar.google.com/scholar?cluster=3089333550231775288&hl=en&as_sdt=0,10
| 3 | 2,019 |
Provably efficient RL with Rich Observations via Latent State Decoding
| 180 |
icml
| 14 | 1 |
2023-06-17 03:10:09.372000
|
https://github.com/Microsoft/StateDecoding
| 28 |
Provably efficient rl with rich observations via latent state decoding
|
https://scholar.google.com/scholar?cluster=17139201255005810211&hl=en&as_sdt=0,14
| 5 | 2,019 |
Task-Agnostic Dynamics Priors for Deep Reinforcement Learning
| 43 |
icml
| 2 | 0 |
2023-06-17 03:10:09.587000
|
https://github.com/yilundu/task_agnostic_dynamics_prior
| 13 |
Task-agnostic dynamics priors for deep reinforcement learning
|
https://scholar.google.com/scholar?cluster=2869858217562916387&hl=en&as_sdt=0,5
| 1 | 2,019 |
Autoregressive Energy Machines
| 52 |
icml
| 12 | 1 |
2023-06-17 03:10:09.802000
|
https://github.com/conormdurkan/autoregressive-energy-machines
| 79 |
Autoregressive energy machines
|
https://scholar.google.com/scholar?cluster=6729811760374247021&hl=en&as_sdt=0,5
| 10 | 2,019 |
Imitating Latent Policies from Observation
| 113 |
icml
| 21 | 1 |
2023-06-17 03:10:10.017000
|
https://github.com/ashedwards/ILPO
| 71 |
Imitating latent policies from observation
|
https://scholar.google.com/scholar?cluster=16539609081927748607&hl=en&as_sdt=0,5
| 9 | 2,019 |
On the Connection Between Adversarial Robustness and Saliency Map Interpretability
| 128 |
icml
| 1 | 0 |
2023-06-17 03:10:10.233000
|
https://github.com/cetmann/robustness-interpretability
| 15 |
On the connection between adversarial robustness and saliency map interpretability
|
https://scholar.google.com/scholar?cluster=9006157315043198858&hl=en&as_sdt=0,47
| 1 | 2,019 |
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap
| 31 |
icml
| 2 | 0 |
2023-06-17 03:10:10.450000
|
https://github.com/edfong/npl
| 8 |
Scalable nonparametric sampling from multimodal posteriors with the posterior bootstrap
|
https://scholar.google.com/scholar?cluster=14627195645565170893&hl=en&as_sdt=0,5
| 2 | 2,019 |
Approximating Orthogonal Matrices with Effective Givens Factorization
| 15 |
icml
| 3 | 0 |
2023-06-17 03:10:10.665000
|
https://github.com/tfrerix/givens-factorization
| 7 |
Approximating orthogonal matrices with effective Givens factorization
|
https://scholar.google.com/scholar?cluster=16649468225264145943&hl=en&as_sdt=0,5
| 0 | 2,019 |
MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement
| 227 |
icml
| 30 | 12 |
2023-06-17 03:10:10.881000
|
https://github.com/JasonSWFu/MetricGAN
| 119 |
Metricgan: Generative adversarial networks based black-box metric scores optimization for speech enhancement
|
https://scholar.google.com/scholar?cluster=10740262477107408585&hl=en&as_sdt=0,39
| 3 | 2,019 |
Off-Policy Deep Reinforcement Learning without Exploration
| 944 |
icml
| 127 | 4 |
2023-06-17 03:10:11.105000
|
https://github.com/sfujim/BCQ
| 508 |
Off-policy deep reinforcement learning without exploration
|
https://scholar.google.com/scholar?cluster=13735420516544008547&hl=en&as_sdt=0,33
| 6 | 2,019 |
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation
| 100 |
icml
| 14 | 3 |
2023-06-17 03:10:11.320000
|
https://github.com/ShaniGam/RL-GAN
| 44 |
Transfer learning for related reinforcement learning tasks via image-to-image translation
|
https://scholar.google.com/scholar?cluster=9611056051873190205&hl=en&as_sdt=0,5
| 4 | 2,019 |
Graph U-Nets
| 839 |
icml
| 91 | 8 |
2023-06-17 03:10:11.537000
|
https://github.com/HongyangGao/gunet
| 445 |
Graph u-nets
|
https://scholar.google.com/scholar?cluster=2250116536319373587&hl=en&as_sdt=0,5
| 11 | 2,019 |
Deep Generative Learning via Variational Gradient Flow
| 21 |
icml
| 7 | 2 |
2023-06-17 03:10:11.756000
|
https://github.com/xjtuygao/VGrow
| 12 |
Deep generative learning via variational gradient flow
|
https://scholar.google.com/scholar?cluster=13167225334345346820&hl=en&as_sdt=0,5
| 1 | 2,019 |
Optimal Mini-Batch and Step Sizes for SAGA
| 35 |
icml
| 9 | 0 |
2023-06-17 03:10:11.975000
|
https://github.com/gowerrobert/StochOpt.jl
| 15 |
Optimal mini-batch and step sizes for SAGA
|
https://scholar.google.com/scholar?cluster=14147185624190732996&hl=en&as_sdt=0,11
| 2 | 2,019 |
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
| 228 |
icml
| 13 | 4 |
2023-06-17 03:10:12.228000
|
https://github.com/geifmany/SelectiveNet
| 44 |
Selectivenet: A deep neural network with an integrated reject option
|
https://scholar.google.com/scholar?cluster=3455752188101558663&hl=en&as_sdt=0,23
| 6 | 2,019 |
Data Shapley: Equitable Valuation of Data for Machine Learning
| 441 |
icml
| 60 | 7 |
2023-06-17 03:10:12.445000
|
https://github.com/amiratag/DataShapley
| 212 |
Data shapley: Equitable valuation of data for machine learning
|
https://scholar.google.com/scholar?cluster=7645060584356925514&hl=en&as_sdt=0,5
| 11 | 2,019 |
Amortized Monte Carlo Integration
| 6 |
icml
| 1 | 0 |
2023-06-17 03:10:12.660000
|
https://github.com/talesa/amci
| 15 |
Amortized monte carlo integration
|
https://scholar.google.com/scholar?cluster=7430114062861179606&hl=en&as_sdt=0,14
| 3 | 2,019 |
A Statistical Investigation of Long Memory in Language and Music
| 19 |
icml
| 2 | 0 |
2023-06-17 03:10:12.877000
|
https://github.com/alecgt/RNN_long_memory
| 8 |
A statistical investigation of long memory in language and music
|
https://scholar.google.com/scholar?cluster=3204260135600784159&hl=en&as_sdt=0,44
| 4 | 2,019 |
Automatic Posterior Transformation for Likelihood-Free Inference
| 180 |
icml
| 29 | 0 |
2023-06-17 03:10:13.092000
|
https://github.com/mackelab/delfi
| 71 |
Automatic posterior transformation for likelihood-free inference
|
https://scholar.google.com/scholar?cluster=9520658637115522401&hl=en&as_sdt=0,10
| 14 | 2,019 |
Multi-Object Representation Learning with Iterative Variational Inference
| 385 |
icml
| 2,436 | 170 |
2023-06-17 03:10:13.314000
|
https://github.com/deepmind/deepmind-research
| 11,905 |
Multi-object representation learning with iterative variational inference
|
https://scholar.google.com/scholar?cluster=213712144958725221&hl=en&as_sdt=0,11
| 336 | 2,019 |
An Investigation of Model-Free Planning
| 79 |
icml
| 15 | 1 |
2023-06-17 03:10:13.533000
|
https://github.com/deepmind/boxoban-levels
| 54 |
An investigation of model-free planning
|
https://scholar.google.com/scholar?cluster=7566080617462830679&hl=en&as_sdt=0,9
| 9 | 2,019 |
Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops
| 10 |
icml
| 2 | 0 |
2023-06-17 03:10:13.764000
|
https://github.com/limorigu/Cockamamie-Gobbledegook
| 6 |
Humor in word embeddings: Cockamamie gobbledegook for nincompoops
|
https://scholar.google.com/scholar?cluster=13364498492064893478&hl=en&as_sdt=0,33
| 2 | 2,019 |
Simple Black-box Adversarial Attacks
| 377 |
icml
| 54 | 0 |
2023-06-17 03:10:13.995000
|
https://github.com/cg563/simple-blackbox-attack
| 172 |
Simple black-box adversarial attacks
|
https://scholar.google.com/scholar?cluster=14524309362525785070&hl=en&as_sdt=0,5
| 5 | 2,019 |
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs
| 217 |
icml
| 17 | 0 |
2023-06-17 03:10:14.210000
|
https://github.com/nju-websoft/RSN
| 97 |
Learning to exploit long-term relational dependencies in knowledge graphs
|
https://scholar.google.com/scholar?cluster=13843373750336430796&hl=en&as_sdt=0,5
| 8 | 2,019 |
On The Power of Curriculum Learning in Training Deep Networks
| 330 |
icml
| 24 | 6 |
2023-06-17 03:10:14.426000
|
https://github.com/GuyHacohen/curriculum_learning
| 88 |
On the power of curriculum learning in training deep networks
|
https://scholar.google.com/scholar?cluster=13645945393876441822&hl=en&as_sdt=0,39
| 3 | 2,019 |
Learning Latent Dynamics for Planning from Pixels
| 1,075 |
icml
| 203 | 4 |
2023-06-17 03:10:14.641000
|
https://github.com/google-research/planet
| 1,130 |
Learning latent dynamics for planning from pixels
|
https://scholar.google.com/scholar?cluster=17717536865000191198&hl=en&as_sdt=0,44
| 47 | 2,019 |
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning
| 19 |
icml
| 3 | 0 |
2023-06-17 03:10:14.855000
|
https://github.com/seungyulhan/disc
| 8 |
Dimension-wise importance sampling weight clipping for sample-efficient reinforcement learning
|
https://scholar.google.com/scholar?cluster=17087407211234698411&hl=en&as_sdt=0,33
| 1 | 2,019 |
Importance Sampling Policy Evaluation with an Estimated Behavior Policy
| 57 |
icml
| 5 | 1 |
2023-06-17 03:10:15.071000
|
https://github.com/LARG/regression-importance-sampling
| 8 |
Importance sampling policy evaluation with an estimated behavior policy
|
https://scholar.google.com/scholar?cluster=11718610357007396139&hl=en&as_sdt=0,23
| 4 | 2,019 |
Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications
| 79 |
icml
| 2 | 0 |
2023-06-17 03:10:15.312000
|
https://github.com/crharshaw/submodular-minus-linear
| 5 |
Submodular maximization beyond non-negativity: Guarantees, fast algorithms, and applications
|
https://scholar.google.com/scholar?cluster=4032047436455480189&hl=en&as_sdt=0,5
| 2 | 2,019 |
Provably Efficient Maximum Entropy Exploration
| 203 |
icml
| 0 | 0 |
2023-06-17 03:10:15.527000
|
https://github.com/abbyvansoest/maxent_ant
| 8 |
Provably efficient maximum entropy exploration
|
https://scholar.google.com/scholar?cluster=7107307515820944527&hl=en&as_sdt=0,5
| 3 | 2,019 |
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
| 51 |
icml
| 2 | 0 |
2023-06-17 03:10:15.742000
|
https://github.com/nvedant07/effort_reward_fairness
| 3 |
On the long-term impact of algorithmic decision policies: Effort unfairness and feature segregation through social learning
|
https://scholar.google.com/scholar?cluster=17715435590222166097&hl=en&as_sdt=0,5
| 2 | 2,019 |
Using Pre-Training Can Improve Model Robustness and Uncertainty
| 563 |
icml
| 15 | 3 |
2023-06-17 03:10:15.958000
|
https://github.com/hendrycks/pre-training
| 92 |
Using pre-training can improve model robustness and uncertainty
|
https://scholar.google.com/scholar?cluster=12052219296634461852&hl=en&as_sdt=0,39
| 6 | 2,019 |
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
| 353 |
icml
| 86 | 10 |
2023-06-17 03:10:16.194000
|
https://github.com/arcelien/pba
| 502 |
Population based augmentation: Efficient learning of augmentation policy schedules
|
https://scholar.google.com/scholar?cluster=9297667920061606267&hl=en&as_sdt=0,34
| 20 | 2,019 |
Connectivity-Optimized Representation Learning via Persistent Homology
| 65 |
icml
| 5 | 0 |
2023-06-17 03:10:16.408000
|
https://github.com/c-hofer/COREL_icml2019
| 10 |
Connectivity-optimized representation learning via persistent homology
|
https://scholar.google.com/scholar?cluster=6723358631694302455&hl=en&as_sdt=0,33
| 4 | 2,019 |
Emerging Convolutions for Generative Normalizing Flows
| 90 |
icml
| 4 | 2 |
2023-06-17 03:10:16.622000
|
https://github.com/ehoogeboom/emerging
| 39 |
Emerging convolutions for generative normalizing flows
|
https://scholar.google.com/scholar?cluster=17212015756232898698&hl=en&as_sdt=0,11
| 4 | 2,019 |
Parameter-Efficient Transfer Learning for NLP
| 1,330 |
icml
| 39 | 7 |
2023-06-17 03:10:16.837000
|
https://github.com/google-research/adapter-bert
| 399 |
Parameter-efficient transfer learning for NLP
|
https://scholar.google.com/scholar?cluster=18111543891993452201&hl=en&as_sdt=0,33
| 9 | 2,019 |
Unsupervised Deep Learning by Neighbourhood Discovery
| 138 |
icml
| 19 | 1 |
2023-06-17 03:10:17.052000
|
https://github.com/raymond-sci/AND
| 148 |
Unsupervised deep learning by neighbourhood discovery
|
https://scholar.google.com/scholar?cluster=2594287551241248539&hl=en&as_sdt=0,47
| 6 | 2,019 |
Stable and Fair Classification
| 59 |
icml
| 0 | 3 |
2023-06-17 03:10:17.267000
|
https://github.com/huanglx12/Stable-Fair-Classification
| 1 |
Stable and fair classification
|
https://scholar.google.com/scholar?cluster=6209492851752994222&hl=en&as_sdt=0,33
| 2 | 2,019 |
HexaGAN: Generative Adversarial Nets for Real World Classification
| 43 |
icml
| 4 | 3 |
2023-06-17 03:10:17.483000
|
https://github.com/shinyflight/HexaGAN
| 20 |
Hexagan: Generative adversarial nets for real world classification
|
https://scholar.google.com/scholar?cluster=9625100105337863533&hl=en&as_sdt=0,39
| 4 | 2,019 |
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
| 26 |
icml
| 4 | 0 |
2023-06-17 03:10:17.697000
|
https://github.com/ialong/GPt
| 19 |
Overcoming mean-field approximations in recurrent Gaussian process models
|
https://scholar.google.com/scholar?cluster=13109450737746036374&hl=en&as_sdt=0,5
| 5 | 2,019 |
Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size!
| 5 |
icml
| 1 | 0 |
2023-06-17 03:10:17.912000
|
https://github.com/nbip/ppca_ICML2019
| 1 |
Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size!
|
https://scholar.google.com/scholar?cluster=809292088427670370&hl=en&as_sdt=0,5
| 0 | 2,019 |
Actor-Attention-Critic for Multi-Agent Reinforcement Learning
| 577 |
icml
| 152 | 10 |
2023-06-17 03:10:18.127000
|
https://github.com/shariqiqbal2810/MAAC
| 531 |
Actor-attention-critic for multi-agent reinforcement learning
|
https://scholar.google.com/scholar?cluster=241844530313281803&hl=en&as_sdt=0,14
| 7 | 2,019 |
Complementary-Label Learning for Arbitrary Losses and Models
| 72 |
icml
| 16 | 0 |
2023-06-17 03:10:18.341000
|
https://github.com/takashiishida/comp
| 41 |
Complementary-label learning for arbitrary losses and models
|
https://scholar.google.com/scholar?cluster=4663196775584030091&hl=en&as_sdt=0,5
| 1 | 2,019 |
Learning What and Where to Transfer
| 118 |
icml
| 48 | 2 |
2023-06-17 03:10:18.558000
|
https://github.com/alinlab/L2T-ww
| 246 |
Learning what and where to transfer
|
https://scholar.google.com/scholar?cluster=12979255639867638665&hl=en&as_sdt=0,5
| 8 | 2,019 |
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
| 337 |
icml
| 121 | 35 |
2023-06-17 03:10:18.775000
|
https://github.com/eugenevinitsky/sequential_social_dilemma_games
| 332 |
Social influence as intrinsic motivation for multi-agent deep reinforcement learning
|
https://scholar.google.com/scholar?cluster=13693459800833279358&hl=en&as_sdt=0,44
| 13 | 2,019 |
Learning Discrete and Continuous Factors of Data via Alternating Disentanglement
| 28 |
icml
| 9 | 1 |
2023-06-17 03:10:18.992000
|
https://github.com/snu-mllab/DisentanglementICML19
| 22 |
Learning discrete and continuous factors of data via alternating disentanglement
|
https://scholar.google.com/scholar?cluster=14742637203782847188&hl=en&as_sdt=0,33
| 4 | 2,019 |
Neural Logic Reinforcement Learning
| 50 |
icml
| 27 | 1 |
2023-06-17 03:10:19.206000
|
https://github.com/ZhengyaoJiang/NLRL
| 71 |
Neural logic reinforcement learning
|
https://scholar.google.com/scholar?cluster=18074632043038701502&hl=en&as_sdt=0,41
| 4 | 2,019 |
Kernel Mean Matching for Content Addressability of GANs
| 8 |
icml
| 4 | 0 |
2023-06-17 03:10:19.434000
|
https://github.com/wittawatj/cadgan
| 22 |
Kernel mean matching for content addressability of GANs
|
https://scholar.google.com/scholar?cluster=235365843120524307&hl=en&as_sdt=0,5
| 7 | 2,019 |
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
| 381 |
icml
| 9 | 2 |
2023-06-17 03:10:19.663000
|
https://github.com/epfml/error-feedback-SGD
| 24 |
Error feedback fixes signsgd and other gradient compression schemes
|
https://scholar.google.com/scholar?cluster=15067189376913629578&hl=en&as_sdt=0,36
| 6 | 2,019 |
Riemannian adaptive stochastic gradient algorithms on matrix manifolds
| 44 |
icml
| 19 | 0 |
2023-06-17 03:10:19.878000
|
https://github.com/hiroyuki-kasai/RSOpt
| 55 |
Riemannian adaptive stochastic gradient algorithms on matrix manifolds
|
https://scholar.google.com/scholar?cluster=11814345447980112497&hl=en&as_sdt=0,39
| 5 | 2,019 |
Neural Inverse Knitting: From Images to Manufacturing Instructions
| 27 |
icml
| 7 | 6 |
2023-06-17 03:10:20.093000
|
https://github.com/xionluhnis/neural_inverse_knitting
| 39 |
Neural inverse knitting: from images to manufacturing instructions
|
https://scholar.google.com/scholar?cluster=15939506219703518176&hl=en&as_sdt=0,5
| 7 | 2,019 |
Processing Megapixel Images with Deep Attention-Sampling Models
| 54 |
icml
| 18 | 15 |
2023-06-17 03:10:20.309000
|
https://github.com/idiap/attention-sampling
| 91 |
Processing megapixel images with deep attention-sampling models
|
https://scholar.google.com/scholar?cluster=16495958235848738135&hl=en&as_sdt=0,5
| 9 | 2,019 |
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
| 183 |
icml
| 8 | 1 |
2023-06-17 03:10:20.524000
|
https://github.com/yigitcankaya/Shallow-Deep-Networks
| 33 |
Shallow-deep networks: Understanding and mitigating network overthinking
|
https://scholar.google.com/scholar?cluster=6970216830123198900&hl=en&as_sdt=0,3
| 1 | 2,019 |
Collaborative Evolutionary Reinforcement Learning
| 105 |
icml
| 23 | 3 |
2023-06-17 03:10:20.740000
|
https://github.com/intelai/cerl
| 71 |
Collaborative evolutionary reinforcement learning
|
https://scholar.google.com/scholar?cluster=17431562445096471732&hl=en&as_sdt=0,43
| 12 | 2,019 |
EMI: Exploration with Mutual Information
| 83 |
icml
| 11 | 0 |
2023-06-17 03:10:20.958000
|
https://github.com/snu-mllab/EMI
| 32 |
Emi: Exploration with mutual information
|
https://scholar.google.com/scholar?cluster=13544760374723251277&hl=en&as_sdt=0,19
| 5 | 2,019 |
FloWaveNet : A Generative Flow for Raw Audio
| 174 |
icml
| 113 | 4 |
2023-06-17 03:10:21.202000
|
https://github.com/ksw0306/FloWaveNet
| 494 |
FloWaveNet: A generative flow for raw audio
|
https://scholar.google.com/scholar?cluster=6708907651291228140&hl=en&as_sdt=0,34
| 43 | 2,019 |
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables
| 69 |
icml
| 40 | 4 |
2023-06-17 03:10:21.418000
|
https://github.com/fhkingma/bitswap
| 239 |
Bit-swap: Recursive bits-back coding for lossless compression with hierarchical latent variables
|
https://scholar.google.com/scholar?cluster=12443881008782599419&hl=en&as_sdt=0,5
| 9 | 2,019 |
CompILE: Compositional Imitation Learning and Execution
| 88 |
icml
| 33 | 0 |
2023-06-17 03:10:21.633000
|
https://github.com/tkipf/compile
| 104 |
Compile: Compositional imitation learning and execution
|
https://scholar.google.com/scholar?cluster=12302759254570528216&hl=en&as_sdt=0,22
| 5 | 2,019 |
Fair k-Center Clustering for Data Summarization
| 138 |
icml
| 4 | 0 |
2023-06-17 03:10:21.849000
|
https://github.com/matthklein/fair_k_center_clustering
| 10 |
Fair k-center clustering for data summarization
|
https://scholar.google.com/scholar?cluster=10384783714256817355&hl=en&as_sdt=0,5
| 3 | 2,019 |
Guarantees for Spectral Clustering with Fairness Constraints
| 122 |
icml
| 1 | 0 |
2023-06-17 03:10:22.065000
|
https://github.com/matthklein/fair_spectral_clustering
| 7 |
Guarantees for spectral clustering with fairness constraints
|
https://scholar.google.com/scholar?cluster=10455657164331034065&hl=en&as_sdt=0,5
| 2 | 2,019 |
POPQORN: Quantifying Robustness of Recurrent Neural Networks
| 86 |
icml
| 11 | 0 |
2023-06-17 03:10:22.280000
|
https://github.com/ZhaoyangLyu/POPQORN
| 45 |
POPQORN: Quantifying robustness of recurrent neural networks
|
https://scholar.google.com/scholar?cluster=2942353004594500868&hl=en&as_sdt=0,5
| 5 | 2,019 |
Robust Learning from Untrusted Sources
| 61 |
icml
| 4 | 0 |
2023-06-17 03:10:22.495000
|
https://github.com/NikolaKon1994/Robust-Learning-from-Untrusted-Sources
| 15 |
Robust learning from untrusted sources
|
https://scholar.google.com/scholar?cluster=4366540847036601471&hl=en&as_sdt=0,5
| 2 | 2,019 |
Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement
| 128 |
icml
| 6 | 0 |
2023-06-17 03:10:22.711000
|
https://github.com/wouterkool/stochastic-beam-search
| 90 |
Stochastic beams and where to find them: The gumbel-top-k trick for sampling sequences without replacement
|
https://scholar.google.com/scholar?cluster=13121847178128779153&hl=en&as_sdt=0,33
| 7 | 2,019 |
Loss Landscapes of Regularized Linear Autoencoders
| 70 |
icml
| 12 | 2 |
2023-06-17 03:10:22.926000
|
https://github.com/danielkunin/Regularized-Linear-Autoencoders
| 139 |
Loss landscapes of regularized linear autoencoders
|
https://scholar.google.com/scholar?cluster=15048938764743692524&hl=en&as_sdt=0,47
| 8 | 2,019 |
A Large-Scale Study on Regularization and Normalization in GANs
| 174 |
icml
| 322 | 16 |
2023-06-17 03:10:23.141000
|
https://github.com/google/compare_gan
| 1,814 |
A large-scale study on regularization and normalization in GANs
|
https://scholar.google.com/scholar?cluster=2102263768032678612&hl=en&as_sdt=0,5
| 52 | 2,019 |
Characterizing Well-Behaved vs. Pathological Deep Neural Networks
| 14 |
icml
| 1 | 2 |
2023-06-17 03:10:23.356000
|
https://github.com/alabatie/moments-dnns
| 5 |
Characterizing well-behaved vs. pathological deep neural networks
|
https://scholar.google.com/scholar?cluster=3271469999043438586&hl=en&as_sdt=0,40
| 3 | 2,019 |
Self-Attention Graph Pooling
| 846 |
icml
| 78 | 11 |
2023-06-17 03:10:23.570000
|
https://github.com/inyeoplee77/SAGPool
| 324 |
Self-attention graph pooling
|
https://scholar.google.com/scholar?cluster=8950252210828065007&hl=en&as_sdt=0,10
| 8 | 2,019 |
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
| 777 |
icml
| 85 | 9 |
2023-06-17 03:10:23.785000
|
https://github.com/juho-lee/set_transformer
| 449 |
Set transformer: A framework for attention-based permutation-invariant neural networks
|
https://scholar.google.com/scholar?cluster=564620061424738263&hl=en&as_sdt=0,34
| 13 | 2,019 |
Robust Inference via Generative Classifiers for Handling Noisy Labels
| 93 |
icml
| 5 | 1 |
2023-06-17 03:10:23.999000
|
https://github.com/pokaxpoka/RoGNoisyLabel
| 29 |
Robust inference via generative classifiers for handling noisy labels
|
https://scholar.google.com/scholar?cluster=14567604075585438767&hl=en&as_sdt=0,43
| 3 | 2,019 |
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