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|---|---|---|---|---|---|---|---|---|---|---|---|
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
| 1,208
|
icml
| 122
| 8
|
2023-06-17 04:14:29.523000
|
https://github.com/facebookresearch/barlowtwins
| 886
|
Barlow twins: Self-supervised learning via redundancy reduction
|
https://scholar.google.com/scholar?cluster=5159677840794766125&hl=en&as_sdt=0,47
| 28
| 2,021
|
You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling
| 10
|
icml
| 3
| 2
|
2023-06-17 04:14:29.769000
|
https://github.com/mlpen/yoso
| 13
|
You only sample (almost) once: Linear cost self-attention via bernoulli sampling
|
https://scholar.google.com/scholar?cluster=11877607783928250360&hl=en&as_sdt=0,10
| 2
| 2,021
|
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
| 67
|
icml
| 477
| 18
|
2023-06-17 04:14:29.972000
|
https://github.com/kwai/DouZero
| 3,324
|
Douzero: Mastering doudizhu with self-play deep reinforcement learning
|
https://scholar.google.com/scholar?cluster=10717987879996790788&hl=en&as_sdt=0,33
| 44
| 2,021
|
DORO: Distributional and Outlier Robust Optimization
| 27
|
icml
| 4
| 0
|
2023-06-17 04:14:30.174000
|
https://github.com/RuntianZ/doro
| 25
|
Doro: Distributional and outlier robust optimization
|
https://scholar.google.com/scholar?cluster=7792478456437572549&hl=en&as_sdt=0,6
| 2
| 2,021
|
Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons
| 31
|
icml
| 6
| 0
|
2023-06-17 04:14:30.377000
|
https://github.com/zbh2047/L_inf-dist-net
| 38
|
Towards certifying l-infinity robustness using neural networks with l-inf-dist neurons
|
https://scholar.google.com/scholar?cluster=6201420149183682924&hl=en&as_sdt=0,5
| 2
| 2,021
|
Efficient Lottery Ticket Finding: Less Data is More
| 38
|
icml
| 4
| 0
|
2023-06-17 04:14:30.580000
|
https://github.com/VITA-Group/PrAC-LTH
| 24
|
Efficient lottery ticket finding: Less data is more
|
https://scholar.google.com/scholar?cluster=9030177952981756712&hl=en&as_sdt=0,14
| 8
| 2,021
|
Robust Policy Gradient against Strong Data Corruption
| 22
|
icml
| 0
| 0
|
2023-06-17 04:14:30.783000
|
https://github.com/zhangxz1123/FilteredPolicyGradient
| 4
|
Robust policy gradient against strong data corruption
|
https://scholar.google.com/scholar?cluster=5709291198914313258&hl=en&as_sdt=0,47
| 2
| 2,021
|
PAPRIKA: Private Online False Discovery Rate Control
| 5
|
icml
| 0
| 0
|
2023-06-17 04:14:30.985000
|
https://github.com/wanrongz/PAPRIKA
| 6
|
Paprika: Private online false discovery rate control
|
https://scholar.google.com/scholar?cluster=16053819406696763043&hl=en&as_sdt=0,44
| 2
| 2,021
|
Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation
| 12
|
icml
| 1
| 1
|
2023-06-17 04:14:31.187000
|
https://github.com/AI-secure/PSBA
| 5
|
Progressive-scale boundary blackbox attack via projective gradient estimation
|
https://scholar.google.com/scholar?cluster=2561734592069193549&hl=en&as_sdt=0,11
| 2
| 2,021
|
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization
| 44
|
icml
| 0
| 0
|
2023-06-17 04:14:31.389000
|
https://github.com/YivanZhang/lio
| 9
|
Learning noise transition matrix from only noisy labels via total variation regularization
|
https://scholar.google.com/scholar?cluster=14671082055157503187&hl=en&as_sdt=0,34
| 2
| 2,021
|
Quantile Bandits for Best Arms Identification
| 9
|
icml
| 0
| 0
|
2023-06-17 04:14:31.591000
|
https://github.com/Mengyanz/QSAR
| 0
|
Quantile bandits for best arms identification
|
https://scholar.google.com/scholar?cluster=6809249853640844054&hl=en&as_sdt=0,5
| 2
| 2,021
|
iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients
| 32
|
icml
| 2
| 2
|
2023-06-17 04:14:31.794000
|
https://github.com/MiaoZhang0525/iDARTS
| 9
|
idarts: Differentiable architecture search with stochastic implicit gradients
|
https://scholar.google.com/scholar?cluster=2918201960391178882&hl=en&as_sdt=0,47
| 2
| 2,021
|
Average-Reward Off-Policy Policy Evaluation with Function Approximation
| 23
|
icml
| 658
| 6
|
2023-06-17 04:14:31.997000
|
https://github.com/ShangtongZhang/DeepRL
| 2,943
|
Average-reward off-policy policy evaluation with function approximation
|
https://scholar.google.com/scholar?cluster=12042728594024517731&hl=en&as_sdt=0,10
| 93
| 2,021
|
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration
| 16
|
icml
| 3
| 0
|
2023-06-17 04:14:32.201000
|
https://github.com/NagisaZj/MetaCURE-Public
| 12
|
Metacure: Meta reinforcement learning with empowerment-driven exploration
|
https://scholar.google.com/scholar?cluster=8017350448991384435&hl=en&as_sdt=0,5
| 2
| 2,021
|
World Model as a Graph: Learning Latent Landmarks for Planning
| 41
|
icml
| 2
| 0
|
2023-06-17 04:14:32.404000
|
https://github.com/LunjunZhang/world-model-as-a-graph
| 53
|
World model as a graph: Learning latent landmarks for planning
|
https://scholar.google.com/scholar?cluster=11617385762396360333&hl=en&as_sdt=0,5
| 1
| 2,021
|
Breaking the Deadly Triad with a Target Network
| 29
|
icml
| 658
| 6
|
2023-06-17 04:14:32.607000
|
https://github.com/ShangtongZhang/DeepRL
| 2,943
|
Breaking the deadly triad with a target network
|
https://scholar.google.com/scholar?cluster=3294420755935359524&hl=en&as_sdt=0,5
| 93
| 2,021
|
Dataset Condensation with Differentiable Siamese Augmentation
| 82
|
icml
| 73
| 0
|
2023-06-17 04:14:32.809000
|
https://github.com/VICO-UoE/DatasetCondensation
| 331
|
Dataset condensation with differentiable siamese augmentation
|
https://scholar.google.com/scholar?cluster=14949848395042620640&hl=en&as_sdt=0,5
| 9
| 2,021
|
Calibrate Before Use: Improving Few-shot Performance of Language Models
| 366
|
icml
| 42
| 3
|
2023-06-17 04:14:33.012000
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https://github.com/tonyzhaozh/few-shot-learning
| 273
|
Calibrate before use: Improving few-shot performance of language models
|
https://scholar.google.com/scholar?cluster=8877771337173887679&hl=en&as_sdt=0,5
| 5
| 2,021
|
Few-Shot Neural Architecture Search
| 71
|
icml
| 7
| 3
|
2023-06-17 04:14:33.215000
|
https://github.com/aoiang/few-shot-NAS
| 39
|
Few-shot neural architecture search
|
https://scholar.google.com/scholar?cluster=668653762741709836&hl=en&as_sdt=0,5
| 4
| 2,021
|
How Framelets Enhance Graph Neural Networks
| 41
|
icml
| 13
| 0
|
2023-06-17 04:14:33.416000
|
https://github.com/YuGuangWang/UFG
| 30
|
How framelets enhance graph neural networks
|
https://scholar.google.com/scholar?cluster=13922049936410780570&hl=en&as_sdt=0,44
| 2
| 2,021
|
Probabilistic Sequential Shrinking: A Best Arm Identification Algorithm for Stochastic Bandits with Corruptions
| 6
|
icml
| 0
| 0
|
2023-06-17 04:14:33.619000
|
https://github.com/zixinzh/2021-ICML
| 0
|
Probabilistic sequential shrinking: A best arm identification algorithm for stochastic bandits with corruptions
|
https://scholar.google.com/scholar?cluster=17868833179563071427&hl=en&as_sdt=0,47
| 1
| 2,021
|
Asymmetric Loss Functions for Learning with Noisy Labels
| 25
|
icml
| 4
| 3
|
2023-06-17 04:14:33.822000
|
https://github.com/hitcszx/ALFs
| 28
|
Asymmetric loss functions for learning with noisy labels
|
https://scholar.google.com/scholar?cluster=425870196210326248&hl=en&as_sdt=0,3
| 3
| 2,021
|
Examining and Combating Spurious Features under Distribution Shift
| 34
|
icml
| 3
| 0
|
2023-06-17 04:14:34.026000
|
https://github.com/violet-zct/group-conditional-DRO
| 14
|
Examining and combating spurious features under distribution shift
|
https://scholar.google.com/scholar?cluster=14520135804314510635&hl=en&as_sdt=0,14
| 1
| 2,021
|
Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm
| 11
|
icml
| 3
| 1
|
2023-06-17 04:14:34.228000
|
https://github.com/VITA-Group/SparseADV_Homotopy
| 7
|
Sparse and imperceptible adversarial attack via a homotopy algorithm
|
https://scholar.google.com/scholar?cluster=18221995160833723432&hl=en&as_sdt=0,14
| 8
| 2,021
|
Data-Free Knowledge Distillation for Heterogeneous Federated Learning
| 218
|
icml
| 61
| 12
|
2023-06-17 04:14:34.431000
|
https://github.com/zhuangdizhu/FedGen
| 185
|
Data-free knowledge distillation for heterogeneous federated learning
|
https://scholar.google.com/scholar?cluster=7623989304932004124&hl=en&as_sdt=0,6
| 2
| 2,021
|
Commutative Lie Group VAE for Disentanglement Learning
| 13
|
icml
| 0
| 0
|
2023-06-17 04:14:34.633000
|
https://github.com/zhuxinqimac/CommutativeLieGroupVAE-Pytorch
| 21
|
Commutative lie group vae for disentanglement learning
|
https://scholar.google.com/scholar?cluster=13512230477271020552&hl=en&as_sdt=0,3
| 2
| 2,021
|
Contrastive Learning Inverts the Data Generating Process
| 118
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icml
| 8
| 3
|
2023-06-17 04:14:34.835000
|
https://github.com/brendel-group/cl-ica
| 76
|
Contrastive learning inverts the data generating process
|
https://scholar.google.com/scholar?cluster=6297973976914221052&hl=en&as_sdt=0,6
| 7
| 2,021
|
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
| 30
|
icml
| 2
| 0
|
2023-06-17 04:14:35.040000
|
https://github.com/lmzintgraf/hyperx
| 12
|
Exploration in approximate hyper-state space for meta reinforcement learning
|
https://scholar.google.com/scholar?cluster=598880115896472356&hl=en&as_sdt=0,22
| 2
| 2,021
|
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning
| 21
|
icml
| 6
| 0
|
2023-06-17 04:54:22.079000
|
https://github.com/mominabbass/sharp-maml
| 23
|
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning
|
https://scholar.google.com/scholar?cluster=14950420836477699137&hl=en&as_sdt=0,22
| 1
| 2,022
|
Active Sampling for Min-Max Fairness
| 15
|
icml
| 1
| 1
|
2023-06-17 04:54:22.293000
|
https://github.com/amazon-research/active-sampling-for-minmax-fairness
| 4
|
Active sampling for min-max fairness
|
https://scholar.google.com/scholar?cluster=7250212054919979465&hl=en&as_sdt=0,5
| 6
| 2,022
|
Meaningfully debugging model mistakes using conceptual counterfactual explanations
| 20
|
icml
| 5
| 1
|
2023-06-17 04:54:22.498000
|
https://github.com/mertyg/debug-mistakes-cce
| 70
|
Meaningfully debugging model mistakes using conceptual counterfactual explanations
|
https://scholar.google.com/scholar?cluster=2849569429175172034&hl=en&as_sdt=0,5
| 8
| 2,022
|
On the Convergence of the Shapley Value in Parametric Bayesian Learning Games
| 6
|
icml
| 0
| 0
|
2023-06-17 04:54:22.703000
|
https://github.com/XinyiYS/Parametric-Bayesian-Learning-Games
| 1
|
On the convergence of the Shapley value in parametric Bayesian learning games
|
https://scholar.google.com/scholar?cluster=7727281335591886084&hl=en&as_sdt=0,5
| 1
| 2,022
|
Individual Preference Stability for Clustering
| 2
|
icml
| 1
| 0
|
2023-06-17 04:54:22.909000
|
https://github.com/amazon-research/ip-stability-for-clustering
| 0
|
Individual Preference Stability for Clustering
|
https://scholar.google.com/scholar?cluster=5704874975941768336&hl=en&as_sdt=0,30
| 6
| 2,022
|
Minimum Cost Intervention Design for Causal Effect Identification
| 2
|
icml
| 0
| 0
|
2023-06-17 04:54:23.115000
|
https://github.com/sinaakbarii/min_cost_intervention
| 1
|
Minimum cost intervention design for causal effect identification
|
https://scholar.google.com/scholar?cluster=8464705336757566822&hl=en&as_sdt=0,44
| 1
| 2,022
|
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models
| 60
|
icml
| 4
| 0
|
2023-06-17 04:54:23.322000
|
https://github.com/vanderschaarlab/evaluating-generative-models
| 22
|
How faithful is your synthetic data? sample-level metrics for evaluating and auditing generative models
|
https://scholar.google.com/scholar?cluster=15840878488291944826&hl=en&as_sdt=0,33
| 5
| 2,022
|
Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations
| 2
|
icml
| 0
| 1
|
2023-06-17 04:54:23.528000
|
https://github.com/neuromorphiccomputationresearchprogram/connectionist-symbolic-pseudo-secrets
| 3
|
Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations
|
https://scholar.google.com/scholar?cluster=7363780369551842627&hl=en&as_sdt=0,44
| 1
| 2,022
|
Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer
| 8
|
icml
| 1
| 0
|
2023-06-17 04:54:23.734000
|
https://github.com/lucasalegre/sfols
| 6
|
Optimistic linear support and successor features as a basis for optimal policy transfer
|
https://scholar.google.com/scholar?cluster=130731457432112857&hl=en&as_sdt=0,5
| 2
| 2,022
|
Structured Stochastic Gradient MCMC
| 4
|
icml
| 1
| 0
|
2023-06-17 04:54:23.940000
|
https://github.com/ajboyd2/pytorch_lvi
| 1
|
Structured stochastic gradient MCMC
|
https://scholar.google.com/scholar?cluster=8097612641869986343&hl=en&as_sdt=0,5
| 2
| 2,022
|
XAI for Transformers: Better Explanations through Conservative Propagation
| 16
|
icml
| 12
| 5
|
2023-06-17 04:54:24.145000
|
https://github.com/ameenali/xai_transformers
| 33
|
XAI for transformers: Better explanations through conservative propagation
|
https://scholar.google.com/scholar?cluster=8318067021687688094&hl=en&as_sdt=0,5
| 2
| 2,022
|
Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees
| 1
|
icml
| 0
| 0
|
2023-06-17 04:54:24.349000
|
https://github.com/machinelearningbcam/amrc-for-concept-drift-icml-2022
| 6
|
Minimax classification under concept drift with multidimensional adaptation and performance guarantees
|
https://scholar.google.com/scholar?cluster=6492087255845076443&hl=en&as_sdt=0,5
| 1
| 2,022
|
Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation
| 2
|
icml
| 3
| 8
|
2023-06-17 04:54:24.555000
|
https://github.com/sebastianament/covariancefunctions.jl
| 17
|
Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation
|
https://scholar.google.com/scholar?cluster=17864781963029193260&hl=en&as_sdt=0,11
| 2
| 2,022
|
Towards Understanding Sharpness-Aware Minimization
| 42
|
icml
| 3
| 0
|
2023-06-17 04:54:24.761000
|
https://github.com/tml-epfl/understanding-sam
| 24
|
Towards understanding sharpness-aware minimization
|
https://scholar.google.com/scholar?cluster=18222527206389875127&hl=en&as_sdt=0,3
| 2
| 2,022
|
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
| 27
|
icml
| 5
| 0
|
2023-06-17 04:54:24.966000
|
https://github.com/aangelopoulos/im2im-uq
| 33
|
Image-to-image regression with distribution-free uncertainty quantification and applications in imaging
|
https://scholar.google.com/scholar?cluster=3321497325155679298&hl=en&as_sdt=0,5
| 4
| 2,022
|
Online Balanced Experimental Design
| 0
|
icml
| 1
| 1
|
2023-06-17 04:54:25.171000
|
https://github.com/ddimmery/balancer-package
| 0
|
Online Balanced Experimental Design
|
https://scholar.google.com/scholar?cluster=9578642124774969527&hl=en&as_sdt=0,33
| 1
| 2,022
|
Thresholded Lasso Bandit
| 11
|
icml
| 0
| 0
|
2023-06-17 04:54:25.386000
|
https://github.com/cyberagentailab/thresholded-lasso-bandit
| 5
|
Thresholded lasso bandit
|
https://scholar.google.com/scholar?cluster=2549693999294336180&hl=en&as_sdt=0,44
| 1
| 2,022
|
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model
| 1
|
icml
| 1
| 0
|
2023-06-17 04:54:25.592000
|
https://github.com/BaeHeeSun/NPC
| 16
|
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model
|
https://scholar.google.com/scholar?cluster=8277956937717286777&hl=en&as_sdt=0,5
| 3
| 2,022
|
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification
| 3
|
icml
| 4
| 0
|
2023-06-17 04:54:25.798000
|
https://github.com/junwenbai/c-gmvae
| 23
|
Gaussian mixture variational autoencoder with contrastive learning for multi-label classification
|
https://scholar.google.com/scholar?cluster=9275720515589327599&hl=en&as_sdt=0,3
| 2
| 2,022
|
Certified Neural Network Watermarks with Randomized Smoothing
| 6
|
icml
| 2
| 0
|
2023-06-17 04:54:26.004000
|
https://github.com/arpitbansal297/certified_watermarks
| 9
|
Certified Neural Network Watermarks with Randomized Smoothing
|
https://scholar.google.com/scholar?cluster=2567091061635643130&hl=en&as_sdt=0,22
| 2
| 2,022
|
Learning Stable Classifiers by Transferring Unstable Features
| 5
|
icml
| 0
| 0
|
2023-06-17 04:54:26.215000
|
https://github.com/YujiaBao/Tofu
| 7
|
Learning stable classifiers by transferring unstable features
|
https://scholar.google.com/scholar?cluster=13001665395610981653&hl=en&as_sdt=0,5
| 1
| 2,022
|
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models
| 30
|
icml
| 7
| 1
|
2023-06-17 04:54:26.425000
|
https://github.com/baofff/Extended-Analytic-DPM
| 87
|
Estimating the optimal covariance with imperfect mean in diffusion probabilistic models
|
https://scholar.google.com/scholar?cluster=2323665209976347341&hl=en&as_sdt=0,5
| 2
| 2,022
|
On the Surrogate Gap between Contrastive and Supervised Losses
| 8
|
icml
| 0
| 0
|
2023-06-17 04:54:26.631000
|
https://github.com/nzw0301/gap-contrastive-and-supervised-losses
| 7
|
On the surrogate gap between contrastive and supervised losses
|
https://scholar.google.com/scholar?cluster=17468865477895467662&hl=en&as_sdt=0,33
| 3
| 2,022
|
Imitation Learning by Estimating Expertise of Demonstrators
| 11
|
icml
| 1
| 0
|
2023-06-17 04:54:26.838000
|
https://github.com/stanford-iliad/ileed
| 4
|
Imitation learning by estimating expertise of demonstrators
|
https://scholar.google.com/scholar?cluster=13040919863635608534&hl=en&as_sdt=0,5
| 4
| 2,022
|
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes
| 1
|
icml
| 8
| 2
|
2023-06-17 04:54:27.046000
|
https://github.com/g-benton/volt
| 39
|
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes
|
https://scholar.google.com/scholar?cluster=445432332886185125&hl=en&as_sdt=0,5
| 3
| 2,022
|
Gradient Descent on Neurons and its Link to Approximate Second-order Optimization
| 3
|
icml
| 2
| 0
|
2023-06-17 04:54:27.253000
|
https://github.com/freedbee/neuron_descent_and_kfac
| 1
|
Gradient descent on neurons and its link to approximate second-order optimization
|
https://scholar.google.com/scholar?cluster=4847605706007812580&hl=en&as_sdt=0,14
| 1
| 2,022
|
Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification
| 9
|
icml
| 2
| 0
|
2023-06-17 04:54:27.465000
|
https://github.com/pbevan1/Skin-Deep-Unlearning
| 5
|
Skin deep unlearning: artefact and instrument debiasing in the context of melanoma classification
|
https://scholar.google.com/scholar?cluster=13843943708217895697&hl=en&as_sdt=0,5
| 1
| 2,022
|
Approximate Bayesian Computation with Domain Expert in the Loop
| 4
|
icml
| 0
| 0
|
2023-06-17 04:54:27.671000
|
https://github.com/lfilstro/hitl-abc
| 1
|
Approximate Bayesian Computation with Domain Expert in the Loop
|
https://scholar.google.com/scholar?cluster=17515613516089862675&hl=en&as_sdt=0,33
| 1
| 2,022
|
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning
| 6
|
icml
| 1
| 0
|
2023-06-17 04:54:27.878000
|
https://github.com/albietz/ppsgd
| 5
|
Personalization improves privacy-accuracy tradeoffs in federated learning
|
https://scholar.google.com/scholar?cluster=2803924388956334708&hl=en&as_sdt=0,5
| 1
| 2,022
|
Non-Vacuous Generalisation Bounds for Shallow Neural Networks
| 11
|
icml
| 0
| 0
|
2023-06-17 04:54:28.087000
|
https://github.com/biggs/shallow-nets
| 0
|
Non-vacuous generalisation bounds for shallow neural networks
|
https://scholar.google.com/scholar?cluster=11560382540049939968&hl=en&as_sdt=0,33
| 2
| 2,022
|
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities
| 12
|
icml
| 1
| 0
|
2023-06-17 04:54:28.293000
|
https://github.com/j-cb/breaking_down_ood_detection
| 10
|
Breaking down out-of-distribution detection: Many methods based on ood training data estimate a combination of the same core quantities
|
https://scholar.google.com/scholar?cluster=3629472061640674656&hl=en&as_sdt=0,11
| 1
| 2,022
|
Optimizing Sequential Experimental Design with Deep Reinforcement Learning
| 13
|
icml
| 5
| 0
|
2023-06-17 04:54:28.499000
|
https://github.com/csiro-mlai/RL-BOED
| 5
|
Optimizing Sequential Experimental Design with Deep Reinforcement Learning
|
https://scholar.google.com/scholar?cluster=17698300138792965088&hl=en&as_sdt=0,21
| 3
| 2,022
|
How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective
| 4
|
icml
| 1
| 0
|
2023-06-17 04:54:28.704000
|
https://github.com/fietelab/wide-network-alignment
| 2
|
How to train your wide neural network without backprop: An input-weight alignment perspective
|
https://scholar.google.com/scholar?cluster=9130275033770297216&hl=en&as_sdt=0,33
| 2
| 2,022
|
Lie Point Symmetry Data Augmentation for Neural PDE Solvers
| 17
|
icml
| 5
| 1
|
2023-06-17 04:54:28.916000
|
https://github.com/brandstetter-johannes/lpsda
| 28
|
Lie point symmetry data augmentation for neural pde solvers
|
https://scholar.google.com/scholar?cluster=6135726084743263275&hl=en&as_sdt=0,5
| 2
| 2,022
|
An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees
| 4
|
icml
| 0
| 0
|
2023-06-17 04:54:29.125000
|
https://github.com/glmbraun/csbm
| 2
|
An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees
|
https://scholar.googleusercontent.com/scholar?q=cache:6omcJTzt9pMJ:scholar.google.com/+An+iterative+clustering+algorithm+for+the+Contextual+Stochastic+Block+Model+with+optimality+guarantees&hl=en&as_sdt=0,5
| 1
| 2,022
|
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems
| 10
|
icml
| 6
| 0
|
2023-06-17 04:54:29.332000
|
https://github.com/durstewitzlab/dendplrnn
| 6
|
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems
|
https://scholar.google.com/scholar?cluster=8212489607836330678&hl=en&as_sdt=0,10
| 1
| 2,022
|
Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters
| 7
|
icml
| 2
| 0
|
2023-06-17 04:54:29.543000
|
https://github.com/lmotte/graph-prediction-with-fused-gromov-wasserstein
| 10
|
Learning to predict graphs with fused Gromov-Wasserstein barycenters
|
https://scholar.google.com/scholar?cluster=449987462895486157&hl=en&as_sdt=0,10
| 1
| 2,022
|
Measuring dissimilarity with diffeomorphism invariance
| 1
|
icml
| 1
| 0
|
2023-06-17 04:54:29.752000
|
https://github.com/theophilec/diffy
| 5
|
Measuring dissimilarity with diffeomorphism invariance
|
https://scholar.google.com/scholar?cluster=9356741545436506583&hl=en&as_sdt=0,11
| 1
| 2,022
|
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications
| 7
|
icml
| 3
| 0
|
2023-06-17 04:54:29.960000
|
https://github.com/aCapone1/gauss_proc_unknown_hyp
| 0
|
Gaussian process uniform error bounds with unknown hyperparameters for safety-critical applications
|
https://scholar.google.com/scholar?cluster=10619138412695371190&hl=en&as_sdt=0,5
| 1
| 2,022
|
Burst-Dependent Plasticity and Dendritic Amplification Support Target-Based Learning and Hierarchical Imitation Learning
| 3
|
icml
| 1
| 0
|
2023-06-17 04:54:30.167000
|
https://github.com/cristianocapone/lttb
| 1
|
Burst-dependent plasticity and dendritic amplification support target-based learning and hierarchical imitation learning
|
https://scholar.google.com/scholar?cluster=8004952254033817821&hl=en&as_sdt=0,33
| 1
| 2,022
|
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization
| 9
|
icml
| 0
| 0
|
2023-06-17 04:54:30.374000
|
https://github.com/yaircarmon/recapp
| 0
|
Recapp: Crafting a more efficient catalyst for convex optimization
|
https://scholar.google.com/scholar?cluster=7906072571653012949&hl=en&as_sdt=0,5
| 4
| 2,022
|
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for Everyone
| 59
|
icml
| 1,642
| 38
|
2023-06-17 04:54:30.586000
|
https://github.com/coqui-ai/TTS
| 12,544
|
Yourtts: Towards zero-shot multi-speaker tts and zero-shot voice conversion for everyone
|
https://scholar.google.com/scholar?cluster=8575580251111777245&hl=en&as_sdt=0,5
| 169
| 2,022
|
Stabilizing Off-Policy Deep Reinforcement Learning from Pixels
| 7
|
icml
| 0
| 1
|
2023-06-17 04:54:30.794000
|
https://github.com/aladoro/stabilizing-off-policy-rl
| 8
|
Stabilizing off-policy deep reinforcement learning from pixels
|
https://scholar.google.com/scholar?cluster=14839229722928778219&hl=en&as_sdt=0,5
| 2
| 2,022
|
Robust Imitation Learning against Variations in Environment Dynamics
| 3
|
icml
| 2
| 0
|
2023-06-17 04:54:31.011000
|
https://github.com/jongseongchae/rime
| 4
|
Robust imitation learning against variations in environment dynamics
|
https://scholar.google.com/scholar?cluster=16698148577673896615&hl=en&as_sdt=0,19
| 1
| 2,022
|
UNIREX: A Unified Learning Framework for Language Model Rationale Extraction
| 21
|
icml
| 3
| 2
|
2023-06-17 04:54:31.221000
|
https://github.com/facebookresearch/unirex
| 21
|
Unirex: A unified learning framework for language model rationale extraction
|
https://scholar.google.com/scholar?cluster=7352055260763393065&hl=en&as_sdt=0,21
| 8
| 2,022
|
Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?
| 15
|
icml
| 5
| 1
|
2023-06-17 04:54:31.456000
|
https://github.com/sutd-visual-computing-group/LS-KD-compatibility
| 9
|
Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?
|
https://scholar.google.com/scholar?cluster=7014741791819212008&hl=en&as_sdt=0,39
| 1
| 2,022
|
Learning Bellman Complete Representations for Offline Policy Evaluation
| 2
|
icml
| 0
| 0
|
2023-06-17 04:54:31.662000
|
https://github.com/causalml/bcrl
| 7
|
Learning Bellman Complete Representations for Offline Policy Evaluation
|
https://scholar.google.com/scholar?cluster=6803502920630786381&hl=en&as_sdt=0,33
| 1
| 2,022
|
Sample Efficient Learning of Predictors that Complement Humans
| 5
|
icml
| 2
| 0
|
2023-06-17 04:54:31.869000
|
https://github.com/clinicalml/active_learn_to_defer
| 4
|
Sample efficient learning of predictors that complement humans
|
https://scholar.google.com/scholar?cluster=14604138868272717546&hl=en&as_sdt=0,5
| 9
| 2,022
|
Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets
| 18
|
icml
| 4
| 0
|
2023-06-17 04:54:32.076000
|
https://github.com/vita-group/structure-lth
| 20
|
Coarsening the granularity: Towards structurally sparse lottery tickets
|
https://scholar.google.com/scholar?cluster=11130219439194607083&hl=en&as_sdt=0,5
| 7
| 2,022
|
Learning Domain Adaptive Object Detection with Probabilistic Teacher
| 14
|
icml
| 7
| 4
|
2023-06-17 04:54:32.283000
|
https://github.com/hikvision-research/probabilisticteacher
| 52
|
Learning domain adaptive object detection with probabilistic teacher
|
https://scholar.google.com/scholar?cluster=17755903452096200771&hl=en&as_sdt=0,34
| 5
| 2,022
|
Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning
| 18
|
icml
| 2
| 2
|
2023-06-17 04:54:32.489000
|
https://github.com/HazyResearch/thanos-code
| 16
|
Perfectly balanced: Improving transfer and robustness of supervised contrastive learning
|
https://scholar.google.com/scholar?cluster=4069781946979626386&hl=en&as_sdt=0,5
| 17
| 2,022
|
On Collective Robustness of Bagging Against Data Poisoning
| 5
|
icml
| 1
| 0
|
2023-06-17 04:54:32.695000
|
https://github.com/emiyalzn/icml22-crb
| 2
|
On Collective Robustness of Bagging Against Data Poisoning
|
https://scholar.google.com/scholar?cluster=7671982562316508504&hl=en&as_sdt=0,5
| 1
| 2,022
|
Structure-Aware Transformer for Graph Representation Learning
| 51
|
icml
| 25
| 0
|
2023-06-17 04:54:32.900000
|
https://github.com/borgwardtlab/sat
| 149
|
Structure-aware transformer for graph representation learning
|
https://scholar.google.com/scholar?cluster=4875324713433840142&hl=en&as_sdt=0,5
| 6
| 2,022
|
Optimization-Induced Graph Implicit Nonlinear Diffusion
| 10
|
icml
| 0
| 0
|
2023-06-17 04:54:33.110000
|
https://github.com/7qchen/gind
| 15
|
Optimization-induced graph implicit nonlinear diffusion
|
https://scholar.google.com/scholar?cluster=1600506523476072350&hl=en&as_sdt=0,14
| 2
| 2,022
|
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile
| 2
|
icml
| 0
| 0
|
2023-06-17 04:54:33.326000
|
https://github.com/anfeather/eigen-reptile
| 8
|
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile
|
https://scholar.google.com/scholar?cluster=8530355739289210050&hl=en&as_sdt=0,5
| 1
| 2,022
|
Data-Efficient Double-Win Lottery Tickets from Robust Pre-training
| 2
|
icml
| 0
| 0
|
2023-06-17 04:54:33.531000
|
https://github.com/vita-group/double-win-lth
| 9
|
Data-Efficient Double-Win Lottery Tickets from Robust Pre-training
|
https://scholar.google.com/scholar?cluster=2999471991915534947&hl=en&as_sdt=0,15
| 8
| 2,022
|
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness
| 3
|
icml
| 3
| 0
|
2023-06-17 04:54:33.737000
|
https://github.com/vita-group/linearity-grafting
| 14
|
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness
|
https://scholar.google.com/scholar?cluster=2944620875879702886&hl=en&as_sdt=0,5
| 9
| 2,022
|
Task-aware Privacy Preservation for Multi-dimensional Data
| 4
|
icml
| 1
| 0
|
2023-06-17 04:54:33.943000
|
https://github.com/chengjiangnan/task_aware_privacy
| 2
|
Task-aware privacy preservation for multi-dimensional data
|
https://scholar.google.com/scholar?cluster=12634725104863101184&hl=en&as_sdt=0,14
| 1
| 2,022
|
Adversarially Trained Actor Critic for Offline Reinforcement Learning
| 44
|
icml
| 6
| 0
|
2023-06-17 04:54:34.149000
|
https://github.com/microsoft/atac
| 48
|
Adversarially trained actor critic for offline reinforcement learning
|
https://scholar.google.com/scholar?cluster=8385322441763797566&hl=en&as_sdt=0,22
| 8
| 2,022
|
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests
| 13
|
icml
| 1
| 0
|
2023-06-17 04:54:34.355000
|
https://github.com/victor5as/rieszlearning
| 7
|
Riesznet and forestriesz: Automatic debiased machine learning with neural nets and random forests
|
https://scholar.google.com/scholar?cluster=9961128829212907766&hl=en&as_sdt=0,48
| 1
| 2,022
|
Selective Network Linearization for Efficient Private Inference
| 9
|
icml
| 1
| 0
|
2023-06-17 04:54:34.560000
|
https://github.com/nyu-dice-lab/selective_network_linearization
| 3
|
Selective network linearization for efficient private inference
|
https://scholar.google.com/scholar?cluster=14016452576504224756&hl=en&as_sdt=0,11
| 5
| 2,022
|
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers
| 8
|
icml
| 4
| 0
|
2023-06-17 04:54:34.766000
|
https://github.com/hl-hanlin/gkat
| 7
|
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers
|
https://scholar.google.com/scholar?cluster=1399080390715291897&hl=en&as_sdt=0,33
| 1
| 2,022
|
Context-Aware Drift Detection
| 7
|
icml
| 180
| 127
|
2023-06-17 04:54:34.971000
|
https://github.com/SeldonIO/alibi-detect
| 1,843
|
Context-aware drift detection
|
https://scholar.google.com/scholar?cluster=9993193813631773645&hl=en&as_sdt=0,5
| 35
| 2,022
|
Diffusion bridges vector quantized variational autoencoders
| 5
|
icml
| 1
| 0
|
2023-06-17 04:54:35.176000
|
https://github.com/maxjcohen/diffusion-bridges
| 14
|
Diffusion bridges vector quantized Variational AutoEncoders
|
https://scholar.google.com/scholar?cluster=15768272528622480760&hl=en&as_sdt=0,21
| 4
| 2,022
|
Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model
| 5
|
icml
| 0
| 0
|
2023-06-17 04:54:35.383000
|
https://github.com/JRConti/EthicalModule_vMF
| 1
|
Mitigating gender bias in face recognition using the von mises-fisher mixture model
|
https://scholar.google.com/scholar?cluster=11800206203871099663&hl=en&as_sdt=0,10
| 1
| 2,022
|
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses
| 28
|
icml
| 1
| 0
|
2023-06-17 04:54:35.590000
|
https://github.com/fra31/evaluating-adaptive-test-time-defenses
| 14
|
Evaluating the adversarial robustness of adaptive test-time defenses
|
https://scholar.google.com/scholar?cluster=9007385894917173233&hl=en&as_sdt=0,23
| 2
| 2,022
|
Adversarial Robustness against Multiple and Single $l_p$-Threat Models via Quick Fine-Tuning of Robust Classifiers
| 6
|
icml
| 3
| 1
|
2023-06-17 04:54:35.796000
|
https://github.com/fra31/robust-finetuning
| 14
|
Adversarial Robustness against Multiple and Single -Threat Models via Quick Fine-Tuning of Robust Classifiers
|
https://scholar.google.com/scholar?cluster=14798100310510930510&hl=en&as_sdt=0,5
| 2
| 2,022
|
Continuous Control with Action Quantization from Demonstrations
| 4
|
icml
| 7,322
| 1,026
|
2023-06-17 04:54:36.002000
|
https://github.com/google-research/google-research
| 29,792
|
Continuous Control with Action Quantization from Demonstrations
|
https://scholar.google.com/scholar?cluster=18354958382752460493&hl=en&as_sdt=0,5
| 727
| 2,022
|
Dialog Inpainting: Turning Documents into Dialogs
| 17
|
icml
| 2
| 2
|
2023-06-17 04:54:36.208000
|
https://github.com/google-research/dialog-inpainting
| 85
|
Dialog inpainting: Turning documents into dialogs
|
https://scholar.google.com/scholar?cluster=13888132119591432248&hl=en&as_sdt=0,44
| 8
| 2,022
|
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training
| 22
|
icml
| 8
| 1
|
2023-06-17 04:54:36.421000
|
https://github.com/rong-dai/dispfl
| 34
|
Dispfl: Towards communication-efficient personalized federated learning via decentralized sparse training
|
https://scholar.google.com/scholar?cluster=13590903827423118545&hl=en&as_sdt=0,5
| 2
| 2,022
|
Unsupervised Image Representation Learning with Deep Latent Particles
| 1
|
icml
| 1
| 0
|
2023-06-17 04:54:36.626000
|
https://github.com/taldatech/deep-latent-particles-pytorch
| 21
|
Unsupervised Image Representation Learning with Deep Latent Particles
|
https://scholar.google.com/scholar?cluster=8443981998714808027&hl=en&as_sdt=0,24
| 3
| 2,022
|
Monarch: Expressive Structured Matrices for Efficient and Accurate Training
| 21
|
icml
| 17
| 11
|
2023-06-17 04:54:36.831000
|
https://github.com/hazyresearch/monarch
| 127
|
Monarch: Expressive structured matrices for efficient and accurate training
|
https://scholar.google.com/scholar?cluster=908299519413693348&hl=en&as_sdt=0,48
| 22
| 2,022
|
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