ICML 2023

1,828 papers

Nonparametric Extensions of Randomized Response for Private Confidence Sets

Ian Waudby-Smith, Steven Wu, Aaditya Ramdas

On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs

Richard A Watson, Hengrui Cai, Xinming An, Samuel Mclean, Rui Song

PPG Reloaded: An Empirical Study on What Matters in Phasic Policy Gradient

KAIXIN WANG, Daquan Zhou, Jiashi Feng, Shie Mannor

Reachability-Aware Laplacian Representation in Reinforcement Learning

KAIXIN WANG, Kuangqi Zhou, Jiashi Feng, Bryan Hooi, Xinchao Wang

Offline Meta Reinforcement Learning with In-Distribution Online Adaptation

Jianhao Wang, Jin Zhang, Haozhe Jiang, Junyu Zhang, Liwei Wang

LinSATNet: The Positive Linear Satisfiability Neural Networks

Runzhong Wang, Yunhao Zhang, Ziao Guo, Tianyi Chen, Xiaokang Yang

Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments

Yixuan Wang, Simon Sinong Zhan, Ruochen Jiao, Zhilu Wang, Wanxin Jin

DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning

Zifeng Wang, Zheng Zhan, Yifan Gong, Yucai Shao, Stratis Ioannidis

Robustly Learning a Single Neuron via Sharpness

Puqian Wang, Nikos Zarifis, Ilias Diakonikolas, Jelena Diakonikolas

Network Effects in Performative Prediction Games

Xiaolu Wang, Chung-Yiu Yau, Hoi To Wai

Learning to Bid in Repeated First-Price Auctions with Budgets

Qian Wang, Zongjun Yang, Xiaotie Deng, Yuqing Kong

Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy

Xiyao Wang, Wichayaporn Wongkamjan, Ruonan Jia, Furong Huang

Model-Free Robust Average-Reward Reinforcement Learning

Yue Wang, Alvaro Velasquez, George K. Atia, Ashley Prater-Bennette, Shaofeng Zou

Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning

Tongzhou Wang, Antonio Torralba, Phillip Isola, Amy Zhang

Effective and Efficient Structural Inference with Reservoir Computing

Aoran Wang, Tsz Pan Tong, Jun Pang

GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models

Hanjing Wang, Man-Kit Sit, Congjie He, Ying Wen, Weinan Zhang

A Robust Test for the Stationarity Assumption in Sequential Decision Making

Jitao Wang, Chengchun Shi, Zhenke Wu

InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models

Yingheng Wang, Yair Schiff, Aaron Gokaslan, Weishen Pan, Fei Wang

Estimating Possible Causal Effects with Latent Variables via Adjustment

Tian-Zuo Wang, Tian Qin, Zhi-Hua Zhou

Projected Tensor Power Method for Hypergraph Community Recovery

Jinxin Wang, Yuen-Man Pun, Xiaolu Wang, Peng Wang, Anthony Man-Cho So

Polarity Is All You Need to Learn and Transfer Faster

Qingyang Wang, Michael Alan Powell, Eric W Bridgeford, Ali Geisa, Joshua T Vogelstein

Better Diffusion Models Further Improve Adversarial Training

Zekai Wang, Tianyu Pang, Chao Du, Min Lin, Weiwei Liu

Active Learning based Structural Inference

Aoran Wang, Jun Pang

Deep Temporal Sets with Evidential Reinforced Attentions for Unique Behavioral Pattern Discovery

Dingrong Wang, Deep Shankar Pandey, Krishna Prasad Neupane, Zhiwei Yu, Ervine Zheng

Data Efficient Neural Scaling Law via Model Reusing

Peihao Wang, Rameswar Panda, Zhangyang Wang

Curriculum Co-disentangled Representation Learning across Multiple Environments for Social Recommendation

Xin Wang, Zirui Pan, Yuwei Zhou, Hong Chen, Chendi Ge

GC-Flow: A Graph-Based Flow Network for Effective Clustering

Tianchun Wang, Farzaneh Mirzazadeh, Xiang Zhang, Jie Chen

NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation

Jianfeng Wang, Daniela Massiceti, Xiaolin Hu, Vladimir Pavlovic, Thomas Lukasiewicz

Tighter Information-Theoretic Generalization Bounds from Supersamples

Ziqiao Wang, Yongyi Mao

Direct Parameterization of Lipschitz-Bounded Deep Networks

Ruigang Wang, Ian Manchester

Magneto: A Foundation Transformer

Hongyu Wang, Shuming Ma, Shaohan Huang, Li Dong, Wenhui Wang

CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks

Jue Wang, Yucheng Lu, Binhang Yuan, Beidi Chen, Percy Liang

DIVISION: Memory Efficient Training via Dual Activation Precision

Guanchu Wang, Zirui Liu, Zhimeng Jiang, Ninghao Liu, Na Zou

Slot-VAE: Object-Centric Scene Generation with Slot Attention

Yanbo Wang, Letao Liu, Justin Dauwels

Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap

Hang Wang, Sen Lin, Junshan Zhang

Learning Belief Representations for Partially Observable Deep RL

Andrew Wang, Andrew C Li, Toryn Q. Klassen, Rodrigo Toro Icarte, Sheila A. McIlraith

A/B Testing in Network Data with Covariate-Adaptive Randomization

Jialu Wang, Ping Li, Feifang Hu

FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization

Zhen Wang, Weirui Kuang, Ce Zhang, Bolin Ding, Yaliang Li

Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR

Kaiwen Wang, Nathan Kallus, Wen Sun

Generalized Polyak Step Size for First Order Optimization with Momentum

Xiaoyu Wang, Mikael Johansson, Tong Zhang

CircuitNet: A Generic Neural Network to Realize Universal Circuit Motif Modeling

Yansen Wang, XINYANG JIANG, Kan Ren, Caihua Shan, Xufang Luo

Adaptive Smoothing Gradient Learning for Spiking Neural Networks

Ziming Wang, Runhao Jiang, Shuang Lian, Rui Yan, Huajin Tang

Policy Gradient in Robust MDPs with Global Convergence Guarantee

Qiuhao Wang, Chin Pang Ho, Marek Petrik

On Regularization and Inference with Label Constraints

Kaifu Wang, Hangfeng He, Tin D. Nguyen, Piyush Kumar, Dan Roth

Adversarial Policies Beat Superhuman Go AIs

Tony Tong Wang, Adam Gleave, Tom Tseng, Kellin Pelrine, Nora Belrose

PreNAS: Preferred One-Shot Learning Towards Efficient Neural Architecture Search

Haibin Wang, Ce Ge, Hesen Chen, Xiuyu Sun

A Closer Look at Self-Supervised Lightweight Vision Transformers

Shaoru Wang, Jin Gao, Zeming Li, Xiaoqin Zhang, Weiming Hu

From Hypergraph Energy Functions to Hypergraph Neural Networks

Yuxin Wang, Quan Gan, Xipeng Qiu, Xuanjing Huang, David Wipf

Tilted Sparse Additive Models

Yingjie Wang, Hong Chen, Weifeng Liu, Fengxiang He, Tieliang Gong

Improved Active Multi-Task Representation Learning via Lasso

Yiping Wang, Yifang Chen, Kevin Jamieson, Simon Shaolei Du

Tight Regret Bounds for Single-pass Streaming Multi-armed Bandits

Chen Wang

Bandit Multi-linear DR-Submodular Maximization and Its Applications on Adversarial Submodular Bandits

Zongqi Wan, Jialin Zhang, Wei Chen, Xiaoming Sun, Zhijie Zhang

Multiplier Bootstrap-based Exploration

Runzhe Wan, Haoyu Wei, Branislav Kveton, Rui Song

SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models

Shenghua Wan, Yucen Wang, Minghao Shao, Ruying Chen, De-Chuan Zhan

Poisoning Language Models During Instruction Tuning

Alexander Wan, Eric Wallace, Sheng Shen, Dan Klein

UPSCALE: Unconstrained Channel Pruning

Alvin Wan, Hanxiang Hao, Kaushik Patnaik, Yueyang Xu, Omer Hadad

Investigating the Role of Model-Based Learning in Exploration and Transfer

Jacob C Walker, Eszter Vértes, Yazhe Li, Gabriel Dulac-Arnold, Ankesh Anand

Fast Private Kernel Density Estimation via Locality Sensitive Quantization

Tal Wagner, Yonatan Naamad, Nina Mishra

Leveraging Offline Data in Online Reinforcement Learning

Andrew Wagenmaker, Aldo Pacchiano

On Provable Copyright Protection for Generative Models

Nikhil Vyas, Sham M. Kakade, Boaz Barak

Competitive Gradient Optimization

Abhijeet Vyas, Brian Bullins, Kamyar Azizzadenesheli

Vector Quantized Wasserstein Auto-Encoder

Long Tung Vuong, Trung Le, He Zhao, Chuanxia Zheng, Mehrtash Harandi

Improving l1-Certified Robustness via Randomized Smoothing by Leveraging Box Constraints

Václav Voráček, Matthias Hein

Topological Singularity Detection at Multiple Scales

Julius von Rohrscheidt, Bastian Rieck

Transformers Learn In-Context by Gradient Descent

Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev

Eventual Discounting Temporal Logic Counterfactual Experience Replay

Cameron Voloshin, Abhinav Verma, Yisong Yue

Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models

Luke Vilnis, Yury Zemlyanskiy, Patrick Murray, Alexandre Tachard Passos, Sumit Sanghai

Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single

Paul Vicol

TabLeak: Tabular Data Leakage in Federated Learning

Mark Vero, Mislav Balunovic, Dimitar Iliev Dimitrov, Martin Vechev

AbODE: Ab initio antibody design using conjoined ODEs

Yogesh Verma, Markus Heinonen, Vikas Garg

Multi-Environment Pretraining Enables Transfer to Action Limited Datasets

David Venuto, Sherry Yang, Pieter Abbeel, Doina Precup, Igor Mordatch

Learning the Right Layers a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs

Sara Venturini, Andrea Cristofari, Francesco Rinaldi, Francesco Tudisco

The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms

Anirudh Vemula, Yuda Song, Aarti Singh, Drew Bagnell, Sanjiban Choudhury

Fast (1+ε)-Approximation Algorithms for Binary Matrix Factorization

Ameya Velingker, Maximilian Vötsch, David Woodruff, Samson Zhou

Optimal LP Rounding and Linear-Time Approximation Algorithms for Clustering Edge-Colored Hypergraphs

Nate Veldt

Conditional Tree Matching for Inference-Time Adaptation of Tree Prediction Models

Harshit Varma, Abhijeet Awasthi, Sunita Sarawagi

Best Arm Identification in Multi-Agent Multi-Armed Bandits

Filippo Vannella, Alexandre Proutiere, Jaeseong Jeong

Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time

Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar

Causal Isotonic Calibration for Heterogeneous Treatment Effects

Lars van der Laan, Ernesto Ulloa-Perez, Marco Carone, Alex Luedtke

Trading-Off Payments and Accuracy in Online Classification with Paid Stochastic Experts

Dirk van der Hoeven, Ciara Pike-Burke, Hao Qiu, Nicolò Cesa-bianchi

Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic Data

Boris van Breugel, Zhaozhi Qian, Mihaela van der Schaar

Delayed Feedback in Kernel Bandits

Sattar Vakili, Danyal Ahmed, Alberto Bernacchia, Ciara Pike-Burke

Random Grid Neural Processes for Parametric Partial Differential Equations

Arnaud Vadeboncoeur, Ieva Kazlauskaite, Yanni Papandreou, Fehmi Cirak, Mark Girolami

Semi-Dual Unbalanced Quadratic Optimal Transport: fast statistical rates and convergent algorithm.

Adrien Vacher, Francois-Xavier Vialard

The Monge Gap: A Regularizer to Learn All Transport Maps

Théo Uscidda, Marco Cuturi

Private Federated Learning with Autotuned Compression

Enayat Ullah, Christopher A. Choquette-Choo, Peter Kairouz, Sewoong Oh

From Adaptive Query Release to Machine Unlearning

Enayat Ullah, Raman Arora

Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings

Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun

Submodular Order Functions and Assortment Optimization

Rajan Udwani

Jump-Start Reinforcement Learning

Ikechukwu Uchendu, Ted Xiao, Yao Lu, Banghua Zhu, Mengyuan Yan

Provable Data Subset Selection For Efficient Neural Networks Training

Murad Tukan, Samson Zhou, Alaa Maalouf, Daniela Rus, Vladimir braverman

Expected Gradients of Maxout Networks and Consequences to Parameter Initialization

Hanna Tseran, Guido Montúfar

Representer Point Selection for Explaining Regularized High-dimensional Models

Che-Ping Tsai, Jiong Zhang, Hsiang-Fu Yu, Eli Chien, Cho-jui Hsieh

Mimetic Initialization of Self-Attention Layers

Asher Trockman, J Zico Kolter

Discrete Key-Value Bottleneck

Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Curtis Mozer, Kenji Kawaguchi

Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes

Ba-Hien Tran, Babak Shahbaba, Stephan Mandt, Maurizio Filippone

Robust Weak Supervision with Variational Auto-Encoders

Francesco Tonolini, Nikolaos Aletras, Yunlong Jiao, Gabriella Kazai

Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms

Francesco Tonin, Alex Lambert, Panagiotis Patrinos, Johan Suykens

Distribution Free Domain Generalization

Peifeng Tong, Wu Su, He Li, Jialin Ding, Zhan Haoxiang

Beyond In-Domain Scenarios: Robust Density-Aware Calibration

Christian Tomani, Futa Kai Waseda, Yuesong Shen, Daniel Cremers