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Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks
| 95 |
icml
| 19 | 0 |
2023-06-17 04:14:08.998000
|
https://github.com/aks2203/poisoning-benchmark
| 127 |
Just how toxic is data poisoning? a unified benchmark for backdoor and data poisoning attacks
|
https://scholar.google.com/scholar?cluster=15855049854905847899&hl=en&as_sdt=0,34
| 6 | 2,021 |
Learning Intra-Batch Connections for Deep Metric Learning
| 33 |
icml
| 8 | 2 |
2023-06-17 04:14:09.202000
|
https://github.com/dvl-tum/intra_batch
| 43 |
Learning intra-batch connections for deep metric learning
|
https://scholar.google.com/scholar?cluster=10851391941882516865&hl=en&as_sdt=0,33
| 3 | 2,021 |
Personalized Federated Learning using Hypernetworks
| 124 |
icml
| 24 | 0 |
2023-06-17 04:14:09.405000
|
https://github.com/AvivSham/pFedHN
| 138 |
Personalized federated learning using hypernetworks
|
https://scholar.google.com/scholar?cluster=9364037892005853502&hl=en&as_sdt=0,5
| 4 | 2,021 |
Backdoor Scanning for Deep Neural Networks through K-Arm Optimization
| 49 |
icml
| 4 | 0 |
2023-06-17 04:14:09.607000
|
https://github.com/PurduePAML/K-ARM_Backdoor_Optimization
| 11 |
Backdoor scanning for deep neural networks through k-arm optimization
|
https://scholar.google.com/scholar?cluster=18424002237979010229&hl=en&as_sdt=0,5
| 9 | 2,021 |
State Relevance for Off-Policy Evaluation
| 2 |
icml
| 0 | 0 |
2023-06-17 04:14:09.810000
|
https://github.com/dtak/osiris
| 1 |
State relevance for off-policy evaluation
|
https://scholar.google.com/scholar?cluster=1184988858503207705&hl=en&as_sdt=0,25
| 2 | 2,021 |
Learning Gradient Fields for Molecular Conformation Generation
| 96 |
icml
| 28 | 7 |
2023-06-17 04:14:10.013000
|
https://github.com/DeepGraphLearning/ConfGF
| 131 |
Learning gradient fields for molecular conformation generation
|
https://scholar.google.com/scholar?cluster=1418815604364379894&hl=en&as_sdt=0,47
| 9 | 2,021 |
Deeply-Debiased Off-Policy Interval Estimation
| 22 |
icml
| 3 | 0 |
2023-06-17 04:14:10.216000
|
https://github.com/RunzheStat/D2OPE
| 9 |
Deeply-debiased off-policy interval estimation
|
https://scholar.google.com/scholar?cluster=16793961424384021624&hl=en&as_sdt=0,33
| 2 | 2,021 |
On Characterizing GAN Convergence Through Proximal Duality Gap
| 5 |
icml
| 2 | 1 |
2023-06-17 04:14:10.421000
|
https://github.com/proximal-dg/proximal_dg
| 9 |
On characterizing gan convergence through proximal duality gap
|
https://scholar.google.com/scholar?cluster=16988175738385537443&hl=en&as_sdt=0,44
| 3 | 2,021 |
PopSkipJump: Decision-Based Attack for Probabilistic Classifiers
| 1 |
icml
| 0 | 0 |
2023-06-17 04:14:10.626000
|
https://github.com/cjsg/PopSkipJump
| 4 |
Popskipjump: Decision-based attack for probabilistic classifiers
|
https://scholar.google.com/scholar?cluster=8512283764080476060&hl=en&as_sdt=0,39
| 1 | 2,021 |
Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances
| 31 |
icml
| 1 | 0 |
2023-06-17 04:14:10.856000
|
https://github.com/jbrea/symmetrysaddles.jl
| 0 |
Geometry of the loss landscape in overparameterized neural networks: Symmetries and invariances
|
https://scholar.google.com/scholar?cluster=6069341273217919605&hl=en&as_sdt=0,5
| 2 | 2,021 |
Skew Orthogonal Convolutions
| 34 |
icml
| 6 | 1 |
2023-06-17 04:14:11.067000
|
https://github.com/singlasahil14/SOC
| 12 |
Skew orthogonal convolutions
|
https://scholar.google.com/scholar?cluster=17464482494309423430&hl=en&as_sdt=0,39
| 1 | 2,021 |
Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks
| 2 |
icml
| 3 | 0 |
2023-06-17 04:14:11.274000
|
https://github.com/srsohn/shortest-path-rl
| 11 |
Shortest-path constrained reinforcement learning for sparse reward tasks
|
https://scholar.google.com/scholar?cluster=5761539218622911437&hl=en&as_sdt=0,10
| 5 | 2,021 |
Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving
| 9 |
icml
| 0 | 0 |
2023-06-17 04:14:11.476000
|
https://github.com/ermongroup/fast_feedforward_computation
| 18 |
Accelerating feedforward computation via parallel nonlinear equation solving
|
https://scholar.google.com/scholar?cluster=9587891109353811026&hl=en&as_sdt=0,11
| 7 | 2,021 |
PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration
| 13 |
icml
| 3 | 0 |
2023-06-17 04:14:11.686000
|
https://github.com/yudasong/PCMLP
| 3 |
Pc-mlp: Model-based reinforcement learning with policy cover guided exploration
|
https://scholar.google.com/scholar?cluster=8561706312159715447&hl=en&as_sdt=0,36
| 2 | 2,021 |
Decoupling Representation Learning from Reinforcement Learning
| 220 |
icml
| 326 | 63 |
2023-06-17 04:14:11.889000
|
https://github.com/astooke/rlpyt
| 2,141 |
Decoupling representation learning from reinforcement learning
|
https://scholar.google.com/scholar?cluster=4351064812627090102&hl=en&as_sdt=0,47
| 53 | 2,021 |
Not All Memories are Created Equal: Learning to Forget by Expiring
| 24 |
icml
| 17 | 2 |
2023-06-17 04:14:12.093000
|
https://github.com/facebookresearch/transformer-sequential
| 134 |
Not all memories are created equal: Learning to forget by expiring
|
https://scholar.google.com/scholar?cluster=18323176449983399592&hl=en&as_sdt=0,11
| 10 | 2,021 |
Nondeterminism and Instability in Neural Network Optimization
| 17 |
icml
| 0 | 1 |
2023-06-17 04:14:12.304000
|
https://github.com/ceciliaresearch/nondeterminism_instability
| 1 |
Nondeterminism and instability in neural network optimization
|
https://scholar.google.com/scholar?cluster=3721428237004074314&hl=en&as_sdt=0,44
| 1 | 2,021 |
What Makes for End-to-End Object Detection?
| 79 |
icml
| 74 | 3 |
2023-06-17 04:14:12.506000
|
https://github.com/PeizeSun/OneNet
| 633 |
What makes for end-to-end object detection?
|
https://scholar.google.com/scholar?cluster=17182921757850029040&hl=en&as_sdt=0,4
| 20 | 2,021 |
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
| 29 |
icml
| 2 | 0 |
2023-06-17 04:14:12.709000
|
https://github.com/j3soon/dfac
| 22 |
DFAC framework: Factorizing the value function via quantile mixture for multi-agent distributional Q-learning
|
https://scholar.google.com/scholar?cluster=13269837837943676067&hl=en&as_sdt=0,5
| 3 | 2,021 |
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition
| 5 |
icml
| 0 | 0 |
2023-06-17 04:14:12.911000
|
https://github.com/ssydasheng/Harmonic-Kernel-Decomposition
| 9 |
Scalable variational gaussian processes via harmonic kernel decomposition
|
https://scholar.google.com/scholar?cluster=5527723102830248655&hl=en&as_sdt=0,44
| 1 | 2,021 |
Model-Targeted Poisoning Attacks with Provable Convergence
| 24 |
icml
| 4 | 3 |
2023-06-17 04:14:13.114000
|
https://github.com/suyeecav/model-targeted-poisoning
| 9 |
Model-targeted poisoning attacks with provable convergence
|
https://scholar.google.com/scholar?cluster=1651990358981165914&hl=en&as_sdt=0,5
| 3 | 2,021 |
Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap
| 21 |
icml
| 4 | 1 |
2023-06-17 04:14:13.316000
|
https://github.com/gkswamy98/pillbox
| 16 |
Of moments and matching: A game-theoretic framework for closing the imitation gap
|
https://scholar.google.com/scholar?cluster=7938694148424637226&hl=en&as_sdt=0,49
| 2 | 2,021 |
Parallel tempering on optimized paths
| 10 |
icml
| 2 | 0 |
2023-06-17 04:14:13.520000
|
https://github.com/vittrom/PT-pathoptim
| 2 |
Parallel tempering on optimized paths
|
https://scholar.google.com/scholar?cluster=14697506612657062549&hl=en&as_sdt=0,5
| 1 | 2,021 |
Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training
| 19 |
icml
| 3 | 0 |
2023-06-17 04:14:13.736000
|
https://github.com/stanford-futuredata/sinkhorn-label-allocation
| 50 |
Sinkhorn label allocation: Semi-supervised classification via annealed self-training
|
https://scholar.google.com/scholar?cluster=13645843302447766832&hl=en&as_sdt=0,44
| 7 | 2,021 |
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
| 10 |
icml
| 2 | 0 |
2023-06-17 04:14:13.939000
|
https://github.com/RAO-EPFL/DR-DA
| 3 |
Sequential domain adaptation by synthesizing distributionally robust experts
|
https://scholar.google.com/scholar?cluster=6930689921879255394&hl=en&as_sdt=0,13
| 2 | 2,021 |
T-SCI: A Two-Stage Conformal Inference Algorithm with Guaranteed Coverage for Cox-MLP
| 4 |
icml
| 0 | 0 |
2023-06-17 04:14:14.141000
|
https://github.com/thutzr/Cox
| 1 |
T-sci: A two-stage conformal inference algorithm with guaranteed coverage for cox-mlp
|
https://scholar.google.com/scholar?cluster=1012431253971456969&hl=en&as_sdt=0,39
| 2 | 2,021 |
Moreau-Yosida $f$-divergences
| 2 |
icml
| 0 | 0 |
2023-06-17 04:14:14.358000
|
https://github.com/renyi-ai/moreau-yosida-f-divergences
| 2 |
Moreau-Yosida -divergences
|
https://scholar.google.com/scholar?cluster=3652869154522690970&hl=en&as_sdt=0,5
| 2 | 2,021 |
Training data-efficient image transformers & distillation through attention
| 3,348 |
icml
| 516 | 12 |
2023-06-17 04:14:14.561000
|
https://github.com/facebookresearch/deit
| 3,450 |
Training data-efficient image transformers & distillation through attention
|
https://scholar.google.com/scholar?cluster=16235705232339507184&hl=en&as_sdt=0,48
| 48 | 2,021 |
Conservative Objective Models for Effective Offline Model-Based Optimization
| 36 |
icml
| 7 | 2 |
2023-06-17 04:14:14.764000
|
https://github.com/brandontrabucco/design-baselines
| 42 |
Conservative objective models for effective offline model-based optimization
|
https://scholar.google.com/scholar?cluster=10951629581873877852&hl=en&as_sdt=0,10
| 4 | 2,021 |
On Disentangled Representations Learned from Correlated Data
| 71 |
icml
| 5 | 0 |
2023-06-17 04:14:14.966000
|
https://github.com/ftraeuble/disentanglement_lib
| 10 |
On disentangled representations learned from correlated data
|
https://scholar.google.com/scholar?cluster=10644866140945749570&hl=en&as_sdt=0,33
| 0 | 2,021 |
A New Formalism, Method and Open Issues for Zero-Shot Coordination
| 16 |
icml
| 0 | 0 |
2023-06-17 04:14:15.169000
|
https://github.com/johannestreutlein/op-tie-breaking
| 4 |
A new formalism, method and open issues for zero-shot coordination
|
https://scholar.google.com/scholar?cluster=7081499741440160815&hl=en&as_sdt=0,10
| 1 | 2,021 |
Learning a Universal Template for Few-shot Dataset Generalization
| 49 |
icml
| 136 | 44 |
2023-06-17 04:14:15.371000
|
https://github.com/google-research/meta-dataset
| 698 |
Learning a universal template for few-shot dataset generalization
|
https://scholar.google.com/scholar?cluster=1180369253723418240&hl=en&as_sdt=0,45
| 24 | 2,021 |
Provable Meta-Learning of Linear Representations
| 127 |
icml
| 1 | 0 |
2023-06-17 04:14:15.573000
|
https://github.com/nileshtrip/MTL
| 2 |
Provable meta-learning of linear representations
|
https://scholar.google.com/scholar?cluster=14454744225976907789&hl=en&as_sdt=0,36
| 2 | 2,021 |
LTL2Action: Generalizing LTL Instructions for Multi-Task RL
| 43 |
icml
| 4 | 0 |
2023-06-17 04:14:15.777000
|
https://github.com/LTL2Action/LTL2Action
| 16 |
Ltl2action: Generalizing ltl instructions for multi-task rl
|
https://scholar.google.com/scholar?cluster=14511888964718858114&hl=en&as_sdt=0,5
| 1 | 2,021 |
Online Graph Dictionary Learning
| 32 |
icml
| 6 | 0 |
2023-06-17 04:14:15.981000
|
https://github.com/cedricvincentcuaz/GDL
| 12 |
Online graph dictionary learning
|
https://scholar.google.com/scholar?cluster=7527452774562329300&hl=en&as_sdt=0,33
| 1 | 2,021 |
Efficient Training of Robust Decision Trees Against Adversarial Examples
| 18 |
icml
| 8 | 0 |
2023-06-17 04:14:16.184000
|
https://github.com/tudelft-cda-lab/GROOT
| 18 |
Efficient training of robust decision trees against adversarial examples
|
https://scholar.google.com/scholar?cluster=9227298780298647203&hl=en&as_sdt=0,33
| 5 | 2,021 |
Object Segmentation Without Labels with Large-Scale Generative Models
| 28 |
icml
| 30 | 3 |
2023-06-17 04:14:16.387000
|
https://github.com/anvoynov/BigGANsAreWatching
| 118 |
Object segmentation without labels with large-scale generative models
|
https://scholar.google.com/scholar?cluster=7466808437204273550&hl=en&as_sdt=0,5
| 7 | 2,021 |
Principal Component Hierarchy for Sparse Quadratic Programs
| 3 |
icml
| 0 | 1 |
2023-06-17 04:14:16.591000
|
https://github.com/RVreugdenhil/sparseQP
| 3 |
Principal component hierarchy for sparse quadratic programs
|
https://scholar.google.com/scholar?cluster=2335943370788592099&hl=en&as_sdt=0,5
| 2 | 2,021 |
Safe Reinforcement Learning Using Advantage-Based Intervention
| 26 |
icml
| 5 | 1 |
2023-06-17 04:14:16.794000
|
https://github.com/nolanwagener/safe_rl
| 18 |
Safe reinforcement learning using advantage-based intervention
|
https://scholar.google.com/scholar?cluster=5048043466827651236&hl=en&as_sdt=0,33
| 1 | 2,021 |
Learning and Planning in Average-Reward Markov Decision Processes
| 38 |
icml
| 0 | 0 |
2023-06-17 04:14:16.998000
|
https://github.com/abhisheknaik96/average-reward-methods
| 12 |
Learning and planning in average-reward markov decision processes
|
https://scholar.google.com/scholar?cluster=750901868273869826&hl=en&as_sdt=0,47
| 1 | 2,021 |
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
| 27 |
icml
| 5 | 0 |
2023-06-17 04:14:17.202000
|
https://github.com/xingchenwan/Casmopolitan
| 21 |
Think global and act local: Bayesian optimisation over high-dimensional categorical and mixed search spaces
|
https://scholar.google.com/scholar?cluster=6765216544866118683&hl=en&as_sdt=0,47
| 1 | 2,021 |
Zero-Shot Knowledge Distillation from a Decision-Based Black-Box Model
| 24 |
icml
| 1 | 2 |
2023-06-17 04:14:17.405000
|
https://github.com/zwang84/zsdb3kd
| 10 |
Zero-shot knowledge distillation from a decision-based black-box model
|
https://scholar.google.com/scholar?cluster=7908835679548457764&hl=en&as_sdt=0,33
| 3 | 2,021 |
Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss
| 5 |
icml
| 2 | 0 |
2023-06-17 04:14:17.607000
|
https://github.com/JialiWang12/SPGLS
| 2 |
Fast algorithms for stackelberg prediction game with least squares loss
|
https://scholar.google.com/scholar?cluster=2550353303659094230&hl=en&as_sdt=0,5
| 2 | 2,021 |
Self-Tuning for Data-Efficient Deep Learning
| 35 |
icml
| 14 | 6 |
2023-06-17 04:14:17.810000
|
https://github.com/thuml/Self-Tuning
| 108 |
Self-tuning for data-efficient deep learning
|
https://scholar.google.com/scholar?cluster=3161082086338934038&hl=en&as_sdt=0,5
| 4 | 2,021 |
AlphaNet: Improved Training of Supernets with Alpha-Divergence
| 41 |
icml
| 13 | 0 |
2023-06-17 04:14:18.013000
|
https://github.com/facebookresearch/AlphaNet
| 90 |
Alphanet: Improved training of supernets with alpha-divergence
|
https://scholar.google.com/scholar?cluster=16040812221590233106&hl=en&as_sdt=0,5
| 10 | 2,021 |
SG-PALM: a Fast Physically Interpretable Tensor Graphical Model
| 6 |
icml
| 0 | 0 |
2023-06-17 04:14:18.217000
|
https://github.com/ywa136/sg-palm
| 0 |
Sg-palm: a fast physically interpretable tensor graphical model
|
https://scholar.google.com/scholar?cluster=15846965999647833426&hl=en&as_sdt=0,39
| 2 | 2,021 |
Robust Inference for High-Dimensional Linear Models via Residual Randomization
| 2 |
icml
| 0 | 0 |
2023-06-17 04:14:18.419000
|
https://github.com/atechnicolorskye/rrHDI
| 0 |
Robust inference for high-dimensional linear models via residual randomization
|
https://scholar.google.com/scholar?cluster=7848775259409033077&hl=en&as_sdt=0,5
| 4 | 2,021 |
Optimal Non-Convex Exact Recovery in Stochastic Block Model via Projected Power Method
| 12 |
icml
| 0 | 0 |
2023-06-17 04:14:18.623000
|
https://github.com/peng8wang/ICML2021-PPM-SBM
| 1 |
Optimal non-convex exact recovery in stochastic block model via projected power method
|
https://scholar.google.com/scholar?cluster=2598400261123150872&hl=en&as_sdt=0,5
| 1 | 2,021 |
The Implicit Bias for Adaptive Optimization Algorithms on Homogeneous Neural Networks
| 14 |
icml
| 0 | 0 |
2023-06-17 04:14:18.826000
|
https://github.com/bhwangfy/ICML-2021-Adaptive-Bias
| 0 |
The implicit bias for adaptive optimization algorithms on homogeneous neural networks
|
https://scholar.google.com/scholar?cluster=6329455504055217085&hl=en&as_sdt=0,6
| 1 | 2,021 |
Directional Bias Amplification
| 30 |
icml
| 1 | 0 |
2023-06-17 04:14:19.029000
|
https://github.com/princetonvisualai/directional-bias-amp
| 12 |
Directional bias amplification
|
https://scholar.google.com/scholar?cluster=16389460185229956032&hl=en&as_sdt=0,5
| 3 | 2,021 |
An exact solver for the Weston-Watkins SVM subproblem
| 1 |
icml
| 1 | 0 |
2023-06-17 04:14:19.233000
|
https://github.com/YutongWangUMich/liblinear
| 1 |
An exact solver for the Weston-Watkins SVM subproblem
|
https://scholar.google.com/scholar?cluster=3159763216882198120&hl=en&as_sdt=0,33
| 1 | 2,021 |
UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data
| 68 |
icml
| 1 | 0 |
2023-06-17 04:14:19.435000
|
https://github.com/cywang97/unispeech
| 6 |
Unispeech: Unified speech representation learning with labeled and unlabeled data
|
https://scholar.google.com/scholar?cluster=13435266557122878220&hl=en&as_sdt=0,10
| 0 | 2,021 |
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach
| 14 |
icml
| 0 | 0 |
2023-06-17 04:14:19.638000
|
https://github.com/Kolin96/learning-to-learn
| 5 |
Guarantees for tuning the step size using a learning-to-learn approach
|
https://scholar.google.com/scholar?cluster=14011148372183922163&hl=en&as_sdt=0,47
| 1 | 2,021 |
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation
| 48 |
icml
| 9 | 1 |
2023-06-17 04:14:19.840000
|
https://github.com/AI-secure/multi-task-learning
| 61 |
Bridging multi-task learning and meta-learning: Towards efficient training and effective adaptation
|
https://scholar.google.com/scholar?cluster=5814522177483838670&hl=en&as_sdt=0,5
| 2 | 2,021 |
Robust Asymmetric Learning in POMDPs
| 18 |
icml
| 0 | 0 |
2023-06-17 04:14:20.043000
|
https://github.com/plai-group/a2d
| 6 |
Robust asymmetric learning in pomdps
|
https://scholar.google.com/scholar?cluster=3140825517966878728&hl=en&as_sdt=0,11
| 6 | 2,021 |
Thinking Like Transformers
| 23 |
icml
| 18 | 1 |
2023-06-17 04:14:20.250000
|
https://github.com/tech-srl/RASP
| 204 |
Thinking like transformers
|
https://scholar.google.com/scholar?cluster=18191652199606300845&hl=en&as_sdt=0,33
| 9 | 2,021 |
Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations
| 4 |
icml
| 0 | 4 |
2023-06-17 04:14:20.453000
|
https://github.com/mateuszwilinski/dynamic-message-passing
| 2 |
Prediction-centric learning of independent cascade dynamics from partial observations
|
https://scholar.google.com/scholar?cluster=10502404999928524540&hl=en&as_sdt=0,5
| 2 | 2,021 |
Learning Neural Network Subspaces
| 40 |
icml
| 17 | 1 |
2023-06-17 04:14:20.656000
|
https://github.com/apple/learning-subspaces
| 124 |
Learning neural network subspaces
|
https://scholar.google.com/scholar?cluster=10251875714480398754&hl=en&as_sdt=0,5
| 10 | 2,021 |
Making Paper Reviewing Robust to Bid Manipulation Attacks
| 17 |
icml
| 5 | 0 |
2023-06-17 04:14:20.858000
|
https://github.com/facebookresearch/secure-paper-bidding
| 9 |
Making paper reviewing robust to bid manipulation attacks
|
https://scholar.google.com/scholar?cluster=3106264104832629742&hl=en&as_sdt=0,5
| 9 | 2,021 |
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning
| 25 |
icml
| 4 | 0 |
2023-06-17 04:14:21.061000
|
https://github.com/tonywu95/lime
| 16 |
Lime: Learning inductive bias for primitives of mathematical reasoning
|
https://scholar.google.com/scholar?cluster=6631886312737976055&hl=en&as_sdt=0,18
| 2 | 2,021 |
ChaCha for Online AutoML
| 5 |
icml
| 379 | 173 |
2023-06-17 04:14:21.263000
|
https://github.com/microsoft/FLAML
| 2,517 |
Chacha for online automl
|
https://scholar.google.com/scholar?cluster=15774579199663385941&hl=en&as_sdt=0,39
| 45 | 2,021 |
Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach
| 20 |
icml
| 6 | 0 |
2023-06-17 04:14:21.465000
|
https://github.com/qitianwu/IDCF
| 24 |
Towards open-world recommendation: An inductive model-based collaborative filtering approach
|
https://scholar.google.com/scholar?cluster=13656226067206698249&hl=en&as_sdt=0,5
| 2 | 2,021 |
A Bit More Bayesian: Domain-Invariant Learning with Uncertainty
| 24 |
icml
| 5 | 1 |
2023-06-17 04:14:21.668000
|
https://github.com/zzzx1224/A-Bit-More-Bayesian
| 10 |
A bit more bayesian: Domain-invariant learning with uncertainty
|
https://scholar.google.com/scholar?cluster=8533759072554466832&hl=en&as_sdt=0,5
| 1 | 2,021 |
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks
| 75 |
icml
| 12 | 3 |
2023-06-17 04:14:21.872000
|
https://github.com/AI-secure/CRFL
| 56 |
Crfl: Certifiably robust federated learning against backdoor attacks
|
https://scholar.google.com/scholar?cluster=566297691223350385&hl=en&as_sdt=0,47
| 3 | 2,021 |
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization
| 21 |
icml
| 6 | 0 |
2023-06-17 04:14:22.074000
|
https://github.com/zeke-xie/Positive-Negative-Momentum
| 25 |
Positive-negative momentum: Manipulating stochastic gradient noise to improve generalization
|
https://scholar.google.com/scholar?cluster=9647717968624963089&hl=en&as_sdt=0,23
| 3 | 2,021 |
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming
| 42 |
icml
| 12 | 0 |
2023-06-17 04:14:22.277000
|
https://github.com/MinkaiXu/ConfVAE-ICML21
| 47 |
An end-to-end framework for molecular conformation generation via bilevel programming
|
https://scholar.google.com/scholar?cluster=914718927564575831&hl=en&as_sdt=0,5
| 3 | 2,021 |
Self-supervised Graph-level Representation Learning with Local and Global Structure
| 96 |
icml
| 15 | 3 |
2023-06-17 04:14:22.479000
|
https://github.com/DeepGraphLearning/GraphLoG
| 57 |
Self-supervised graph-level representation learning with local and global structure
|
https://scholar.google.com/scholar?cluster=15360735332012817623&hl=en&as_sdt=0,5
| 7 | 2,021 |
Conformal prediction interval for dynamic time-series
| 38 |
icml
| 18 | 0 |
2023-06-17 04:14:22.682000
|
https://github.com/hamrel-cxu/EnbPI
| 42 |
Conformal prediction interval for dynamic time-series
|
https://scholar.google.com/scholar?cluster=9397887507156986767&hl=en&as_sdt=0,33
| 2 | 2,021 |
KNAS: Green Neural Architecture Search
| 25 |
icml
| 15 | 1 |
2023-06-17 04:14:22.885000
|
https://github.com/jingjing-nlp/knas
| 90 |
KNAS: green neural architecture search
|
https://scholar.google.com/scholar?cluster=636730090425787241&hl=en&as_sdt=0,36
| 2 | 2,021 |
Structured Convolutional Kernel Networks for Airline Crew Scheduling
| 8 |
icml
| 3 | 0 |
2023-06-17 04:14:23.087000
|
https://github.com/Yaakoubi/Struct-CKN
| 5 |
Structured convolutional kernel networks for airline crew scheduling
|
https://scholar.google.com/scholar?cluster=6467944180520163376&hl=en&as_sdt=0,47
| 1 | 2,021 |
Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences
| 1 |
icml
| 0 | 0 |
2023-06-17 04:14:23.289000
|
https://github.com/i-yamane/mediated_uncoupled_learning
| 2 |
Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences
|
https://scholar.google.com/scholar?cluster=17617652020684598053&hl=en&as_sdt=0,21
| 2 | 2,021 |
EL-Attention: Memory Efficient Lossless Attention for Generation
| 2 |
icml
| 41 | 11 |
2023-06-17 04:14:23.492000
|
https://github.com/microsoft/fastseq
| 416 |
El-attention: Memory efficient lossless attention for generation
|
https://scholar.google.com/scholar?cluster=1375858256863771464&hl=en&as_sdt=0,47
| 15 | 2,021 |
Link Prediction with Persistent Homology: An Interactive View
| 23 |
icml
| 1 | 1 |
2023-06-17 04:14:23.695000
|
https://github.com/pkuyzy/TLC-GNN
| 8 |
Link prediction with persistent homology: An interactive view
|
https://scholar.google.com/scholar?cluster=6988958697269886780&hl=en&as_sdt=0,44
| 2 | 2,021 |
CATE: Computation-aware Neural Architecture Encoding with Transformers
| 12 |
icml
| 5 | 1 |
2023-06-17 04:14:23.897000
|
https://github.com/MSU-MLSys-Lab/CATE
| 17 |
Cate: Computation-aware neural architecture encoding with transformers
|
https://scholar.google.com/scholar?cluster=8641165479167437291&hl=en&as_sdt=0,41
| 4 | 2,021 |
On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework
| 12 |
icml
| 2 | 0 |
2023-06-17 04:14:24.100000
|
https://github.com/ZeyuYan/Perceptual-Lossy-Compression
| 10 |
On perceptual lossy compression: The cost of perceptual reconstruction and an optimal training framework
|
https://scholar.google.com/scholar?cluster=3982169689811841911&hl=en&as_sdt=0,36
| 1 | 2,021 |
Graph Neural Networks Inspired by Classical Iterative Algorithms
| 43 |
icml
| 5 | 0 |
2023-06-17 04:14:24.302000
|
https://github.com/FFTYYY/TWIRLS
| 35 |
Graph neural networks inspired by classical iterative algorithms
|
https://scholar.google.com/scholar?cluster=7834297008396631458&hl=en&as_sdt=0,5
| 2 | 2,021 |
Voice2Series: Reprogramming Acoustic Models for Time Series Classification
| 49 |
icml
| 8 | 3 |
2023-06-17 04:14:24.504000
|
https://github.com/huckiyang/Voice2Series-Reprogramming
| 54 |
Voice2series: Reprogramming acoustic models for time series classification
|
https://scholar.google.com/scholar?cluster=436573915483653789&hl=en&as_sdt=0,38
| 2 | 2,021 |
Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
| 166 |
icml
| 178 | 21 |
2023-06-17 04:14:24.706000
|
https://github.com/yangxue0827/RotationDetection
| 1,013 |
Rethinking rotated object detection with gaussian wasserstein distance loss
|
https://scholar.google.com/scholar?cluster=9458084216549029781&hl=en&as_sdt=0,33
| 21 | 2,021 |
Delving into Deep Imbalanced Regression
| 114 |
icml
| 111 | 3 |
2023-06-17 04:14:24.909000
|
https://github.com/YyzHarry/imbalanced-regression
| 642 |
Delving into deep imbalanced regression
|
https://scholar.google.com/scholar?cluster=14041915448985010978&hl=en&as_sdt=0,31
| 18 | 2,021 |
SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks
| 189 |
icml
| 33 | 9 |
2023-06-17 04:14:25.111000
|
https://github.com/ZjjConan/SimAM
| 257 |
Simam: A simple, parameter-free attention module for convolutional neural networks
|
https://scholar.google.com/scholar?cluster=6748424654077587327&hl=en&as_sdt=0,47
| 5 | 2,021 |
Improving Generalization in Meta-learning via Task Augmentation
| 53 |
icml
| 4 | 0 |
2023-06-17 04:14:25.314000
|
https://github.com/huaxiuyao/MetaMix
| 25 |
Improving generalization in meta-learning via task augmentation
|
https://scholar.google.com/scholar?cluster=756197262814969387&hl=en&as_sdt=0,47
| 1 | 2,021 |
Deep Learning for Functional Data Analysis with Adaptive Basis Layers
| 8 |
icml
| 4 | 0 |
2023-06-17 04:14:25.516000
|
https://github.com/jwyyy/AdaFNN
| 18 |
Deep learning for functional data analysis with adaptive basis layers
|
https://scholar.google.com/scholar?cluster=17144943362411304273&hl=en&as_sdt=0,14
| 1 | 2,021 |
Addressing Catastrophic Forgetting in Few-Shot Problems
| 10 |
icml
| 1 | 0 |
2023-06-17 04:14:25.719000
|
https://github.com/pauchingyap/boml
| 4 |
Addressing catastrophic forgetting in few-shot problems
|
https://scholar.google.com/scholar?cluster=5331519649661500119&hl=en&as_sdt=0,11
| 1 | 2,021 |
Break-It-Fix-It: Unsupervised Learning for Program Repair
| 52 |
icml
| 19 | 5 |
2023-06-17 04:14:25.922000
|
https://github.com/michiyasunaga/bifi
| 97 |
Break-it-fix-it: Unsupervised learning for program repair
|
https://scholar.google.com/scholar?cluster=4368697690139646578&hl=en&as_sdt=0,25
| 2 | 2,021 |
Neighborhood Contrastive Learning Applied to Online Patient Monitoring
| 14 |
icml
| 1 | 0 |
2023-06-17 04:14:26.125000
|
https://github.com/ratschlab/ncl
| 15 |
Neighborhood contrastive learning applied to online patient monitoring
|
https://scholar.google.com/scholar?cluster=4664115316667000917&hl=en&as_sdt=0,5
| 7 | 2,021 |
Continuous-time Model-based Reinforcement Learning
| 25 |
icml
| 9 | 0 |
2023-06-17 04:14:26.328000
|
https://github.com/cagatayyildiz/oderl
| 29 |
Continuous-time model-based reinforcement learning
|
https://scholar.google.com/scholar?cluster=14746718008006143630&hl=en&as_sdt=0,33
| 3 | 2,021 |
Path Planning using Neural A* Search
| 48 |
icml
| 39 | 0 |
2023-06-17 04:14:26.588000
|
https://github.com/omron-sinicx/neural-astar
| 112 |
Path planning using neural a* search
|
https://scholar.google.com/scholar?cluster=997109174991202847&hl=en&as_sdt=0,34
| 8 | 2,021 |
SinIR: Efficient General Image Manipulation with Single Image Reconstruction
| 15 |
icml
| 6 | 2 |
2023-06-17 04:14:26.873000
|
https://github.com/YooJiHyeong/SinIR
| 49 |
Sinir: Efficient general image manipulation with single image reconstruction
|
https://scholar.google.com/scholar?cluster=10599627975062939893&hl=en&as_sdt=0,5
| 4 | 2,021 |
Conditional Temporal Neural Processes with Covariance Loss
| 6 |
icml
| 3 | 0 |
2023-06-17 04:14:27.077000
|
https://github.com/boseon-ai/Conditional-Temporal-Neural-Processes-with-Covariance-Loss
| 4 |
Conditional temporal neural processes with covariance loss
|
https://scholar.google.com/scholar?cluster=11587001317959077781&hl=en&as_sdt=0,5
| 1 | 2,021 |
Adversarial Purification with Score-based Generative Models
| 44 |
icml
| 3 | 2 |
2023-06-17 04:14:27.280000
|
https://github.com/jmyoon1/adp
| 19 |
Adversarial purification with score-based generative models
|
https://scholar.google.com/scholar?cluster=1510322463041774819&hl=en&as_sdt=0,44
| 1 | 2,021 |
Federated Continual Learning with Weighted Inter-client Transfer
| 76 |
icml
| 22 | 0 |
2023-06-17 04:14:27.484000
|
https://github.com/wyjeong/FedWeIT
| 67 |
Federated continual learning with weighted inter-client transfer
|
https://scholar.google.com/scholar?cluster=6346174361267860505&hl=en&as_sdt=0,21
| 2 | 2,021 |
Autoencoding Under Normalization Constraints
| 16 |
icml
| 13 | 0 |
2023-06-17 04:14:27.694000
|
https://github.com/swyoon/normalized-autoencoders
| 37 |
Autoencoding under normalization constraints
|
https://scholar.google.com/scholar?cluster=1297005004772257313&hl=en&as_sdt=0,47
| 5 | 2,021 |
Lower-Bounded Proper Losses for Weakly Supervised Classification
| 2 |
icml
| 0 | 0 |
2023-06-17 04:14:27.897000
|
https://github.com/yoshum/lower-bounded-proper-losses
| 2 |
Lower-Bounded Proper Losses for Weakly Supervised Classification
|
https://scholar.google.com/scholar?cluster=17541047076253957367&hl=en&as_sdt=0,5
| 1 | 2,021 |
Graph Contrastive Learning Automated
| 196 |
icml
| 8 | 4 |
2023-06-17 04:14:28.101000
|
https://github.com/Shen-Lab/GraphCL_Automated
| 85 |
Graph contrastive learning automated
|
https://scholar.google.com/scholar?cluster=4319391299971749370&hl=en&as_sdt=0,33
| 3 | 2,021 |
LogME: Practical Assessment of Pre-trained Models for Transfer Learning
| 69 |
icml
| 15 | 6 |
2023-06-17 04:14:28.303000
|
https://github.com/thuml/LogME
| 172 |
Logme: Practical assessment of pre-trained models for transfer learning
|
https://scholar.google.com/scholar?cluster=7398435047749789865&hl=en&as_sdt=0,33
| 5 | 2,021 |
DAGs with No Curl: An Efficient DAG Structure Learning Approach
| 30 |
icml
| 5 | 1 |
2023-06-17 04:14:28.506000
|
https://github.com/fishmoon1234/DAG-NoCurl
| 16 |
Dags with no curl: An efficient dag structure learning approach
|
https://scholar.google.com/scholar?cluster=3161455728562313506&hl=en&as_sdt=0,5
| 2 | 2,021 |
Large Scale Private Learning via Low-rank Reparametrization
| 41 |
icml
| 17 | 0 |
2023-06-17 04:14:28.710000
|
https://github.com/dayu11/Differentially-Private-Deep-Learning
| 72 |
Large scale private learning via low-rank reparametrization
|
https://scholar.google.com/scholar?cluster=10646842759761842433&hl=en&as_sdt=0,33
| 2 | 2,021 |
Federated Composite Optimization
| 38 |
icml
| 3 | 0 |
2023-06-17 04:14:28.914000
|
https://github.com/hongliny/FCO-ICML21
| 9 |
Federated composite optimization
|
https://scholar.google.com/scholar?cluster=10805982907996173478&hl=en&as_sdt=0,34
| 1 | 2,021 |
Three Operator Splitting with a Nonconvex Loss Function
| 5 |
icml
| 0 | 0 |
2023-06-17 04:14:29.117000
|
https://github.com/alpyurtsever/NonconvexTOS
| 1 |
Three operator splitting with a nonconvex loss function
|
https://scholar.google.com/scholar?cluster=14275996016492090770&hl=en&as_sdt=0,14
| 1 | 2,021 |
Learning Binary Decision Trees by Argmin Differentiation
| 10 |
icml
| 1 | 2 |
2023-06-17 04:14:29.320000
|
https://github.com/vzantedeschi/LatentTrees
| 11 |
Learning binary decision trees by argmin differentiation
|
https://scholar.google.com/scholar?cluster=8235159658077202682&hl=en&as_sdt=0,5
| 1 | 2,021 |
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