Wang, Yu-Xiang

128 publications

TMLR 2025 A Proximal Operator for Inducing 2:4-Sparsity Jonas M. Kübler, Yu-Xiang Wang, Shoham Sabach, Navid Ansari, Matthäus Kleindessner, Kailash Budhathoki, Volkan Cevher, George Karypis

NeurIPS 2025 A Technical Report on “Erasing the Invisible”: The 2024 NeurIPS Competition on Stress Testing Image Watermarks Mucong Ding, Bang An, Tahseen Rabbani, Chenghao Deng, Anirudh Satheesh, Souradip Chakraborty, Mehrdad Saberi, Yuxin Wen, Kyle Rui Sang, Aakriti Agrawal, Xuandong Zhao, Mo Zhou, Mary-Anne Hartley, Lei Li, Yu-Xiang Wang, Vishal M. Patel, Soheil Feizi, Tom Goldstein, Furong Huang

ICML 2025 AKORN: Adaptive Knots Generated Online for RegressioN Splines Sunil Madhow, Dheeraj Baby, Yu-Xiang Wang

ICML 2025 Adapting to Linear Separable Subsets with Large-Margin in Differentially Private Learning Erchi Wang, Yuqing Zhu, Yu-Xiang Wang

AISTATS 2025 Adapting to Online Distribution Shifts in Deep Learning: A Black-Box Approach Dheeraj Baby, Boran Han, Shuai Zhang, Cuixiong Hu, Bernie Wang, Yu-Xiang Wang

ICML 2025 Adaptive Estimation and Learning Under Temporal Distribution Shift Dheeraj Baby, Yifei Tang, Hieu Duy Nguyen, Yu-Xiang Wang, Rohit Pyati

CPAL 2025 MoXCo: How I Learned to Stop Exploring and Love My Local Minima? Esha Singh, Shoham Sabach, Yu-Xiang Wang

ICLR 2025 Permute-and-Flip: An Optimally Stable and Watermarkable Decoder for LLMs Xuandong Zhao, Lei Li, Yu-Xiang Wang

ICML 2025 Proxsparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Hongyi Liu, Rajarshi Saha, Zhen Jia, Youngsuk Park, Jiaji Huang, Shoham Sabach, Yu-Xiang Wang, George Karypis

NeurIPS 2025 Purifying Approximate Differential Privacy with Randomized Post-Processing Yingyu Lin, Erchi Wang, Yian Ma, Yu-Xiang Wang

L4DC 2025 Rates for Offline Reinforcement Learning with Adaptively Collected Data Sunil Madhow, Dan Qiao, Ming Yin, Yu-Xiang Wang

NeurIPS 2025 Stable Minima of ReLU Neural Networks Suffer from the Curse of Dimensionality: The Neural Shattering Phenomenon Tongtong Liang, Dan Qiao, Yu-Xiang Wang, Rahul Parhi

ICML 2025 Weak-to-Strong Jailbreaking on Large Language Models Xuandong Zhao, Xianjun Yang, Tianyu Pang, Chao Du, Lei Li, Yu-Xiang Wang, William Yang Wang

CVPR 2024 CPR: Retrieval Augmented Generation for Copyright Protection Aditya Golatkar, Alessandro Achille, Luca Zancato, Yu-Xiang Wang, Ashwin Swaminathan, Stefano Soatto

ICLR 2024 Communication-Efficient Federated Non-Linear Bandit Optimization Chuanhao Li, Chong Liu, Yu-Xiang Wang

ICML 2024 Differentially Private Bias-Term Fine-Tuning of Foundation Models Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis

NeurIPS 2024 Differentially Private Reinforcement Learning with Self-Play Dan Qiao, Yu-Xiang Wang

NeurIPSW 2024 Efficiently Identifying Watermarked Segments in Mixed-Source Texts Xuandong Zhao, Chenwen Liao, Yu-Xiang Wang, Lei Li

ICML 2024 Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games Songtao Feng, Ming Yin, Yu-Xiang Wang, Jing Yang, Yingbin Liang

NeurIPS 2024 Invisible Image Watermarks Are Provably Removable Using Generative AI Xuandong Zhao, Kexun Zhang, Zihao Su, Saastha Vasan, Ilya Grishchenko, Christopher Kruegel, Giovanni Vigna, Yu-Xiang Wang, Lei Li

ICML 2024 Near-Optimal Reinforcement Learning with Self-Play Under Adaptivity Constraints Dan Qiao, Yu-Xiang Wang

NeurIPS 2024 NetworkGym: Reinforcement Learning Environments for Multi-Access Traffic Management in Network Simulation Momin Haider, Ming Yin, Menglei Zhang, Arpit Gupta, Jing Zhu, Yu-Xiang Wang

ICML 2024 Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-Perfect Representation Learning Chendi Wang, Yuqing Zhu, Weijie J Su, Yu-Xiang Wang

NeurIPS 2024 Nonparametric Classification on Low Dimensional Manifolds Using Overparameterized Convolutional Residual Networks Zixuan Zhang, Kaiqi Zhang, Minshuo Chen, Yuma Takeda, Mengdi Wang, Tuo Zhao, Yu-Xiang Wang

NeurIPS 2024 Online Feature Updates Improve Online (Generalized) Label Shift Adaptation Ruihan Wu, Siddhartha Datta, Yi Su, Dheeraj Baby, Yu-Xiang Wang, Kilian Q. Weinberger

NeurIPSW 2024 Permute-and-Flip: An Optimally Stable and Watermarkable Decoder for LLMs Xuandong Zhao, Lei Li, Yu-Xiang Wang

ICML 2024 Pricing with Contextual Elasticity and Heteroscedastic Valuation Jianyu Xu, Yu-Xiang Wang

ICML 2024 Privacy Profiles for Private Selection Antti Koskela, Rachel Emily Redberg, Yu-Xiang Wang

ICLR 2024 Provable Robust Watermarking for AI-Generated Text Xuandong Zhao, Prabhanjan Vijendra Ananth, Lei Li, Yu-Xiang Wang

NeurIPS 2024 Stable Minima Cannot Overfit in Univariate ReLU Networks: Generalization by Large Step Sizes Dan Qiao, Kaiqi Zhang, Esha Singh, Daniel Soudry, Yu-Xiang Wang

ICLR 2024 Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy Yingyu Lin, Yian Ma, Yu-Xiang Wang, Rachel Emily Redberg, Zhiqi Bu

ICMLW 2024 Weak-to-Strong Jailbreaking on Large Language Models Xuandong Zhao, Xianjun Yang, Tianyu Pang, Chao Du, Lei Li, Yu-Xiang Wang, William Yang Wang

NeurIPS 2023 A Privacy-Friendly Approach to Data Valuation Jiachen Wang, Yuqing Zhu, Yu-Xiang Wang, Ruoxi Jia, Prateek Mittal

NeurIPS 2023 Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis

NeurIPSW 2023 Bi-Directional Goal-Conditioning on Single Value Function for State Space Search Problems Vihaan Akshaay Rajendiran, Yu-Xiang Wang, Lei Li

ICLR 2023 Deep Learning Meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive? Kaiqi Zhang, Yu-Xiang Wang

ICML 2023 Differentially Private Optimization on Large Model at Small Cost Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis

AISTATS 2023 Doubly Fair Dynamic Pricing Jianyu Xu, Dan Qiao, Yu-Xiang Wang

AISTATS 2023 Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy Rachel Redberg, Yuqing Zhu, Yu-Xiang Wang

ICMLW 2023 Generative Autoencoders as Watermark Attackers: Analyses of Vulnerabilities and Threats Xuandong Zhao, Kexun Zhang, Yu-Xiang Wang, Lei Li

ICML 2023 Global Optimization with Parametric Function Approximation Chong Liu, Yu-Xiang Wang

NeurIPS 2023 Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners Rachel Redberg, Antti Koskela, Yu-Xiang Wang

NeurIPSW 2023 MoXCo:How I Learned to Stop Exploring and Love My Local Minima? Esha Singh, Shoham Sabach, Yu-Xiang Wang

ICLR 2023 Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation Dan Qiao, Yu-Xiang Wang

AISTATS 2023 Near-Optimal Differentially Private Reinforcement Learning Dan Qiao, Yu-Xiang Wang

UAI 2023 No-Regret Linear Bandits Beyond Realizability Chong Liu, Ming Yin, Yu-Xiang Wang

TMLR 2023 Non-Stationary Contextual Pricing with Safety Constraints Dheeraj Baby, Jianyu Xu, Yu-Xiang Wang

JMLR 2023 Non-Stationary Online Learning with Memory and Non-Stochastic Control Peng Zhao, Yu-Hu Yan, Yu-Xiang Wang, Zhi-Hua Zhou

ICML 2023 Non-Stationary Reinforcement Learning Under General Function Approximation Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang

NeurIPSW 2023 Nonparametric Classification on Low Dimensional Manifolds Using Overparameterized Convolutional Residual Networks Zixuan Zhang, Kaiqi Zhang, Minshuo Chen, Yuma Takeda, Mengdi Wang, Tuo Zhao, Yu-Xiang Wang

ICML 2023 Offline Reinforcement Learning with Closed-Form Policy Improvement Operators Jiachen Li, Edwin Zhang, Ming Yin, Qinxun Bai, Yu-Xiang Wang, William Yang Wang

ICLR 2023 Offline Reinforcement Learning with Differentiable Function Approximation Is Provably Efficient Ming Yin, Mengdi Wang, Yu-Xiang Wang

NeurIPS 2023 Offline Reinforcement Learning with Differential Privacy Dan Qiao, Yu-Xiang Wang

NeurIPS 2023 Online Label Shift: Optimal Dynamic Regret Meets Practical Algorithms Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen, Sivaraman Balakrishnan, Zachary Lipton, Yu-Xiang Wang

NeurIPS 2023 Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation Nikki Lijing Kuang, Ming Yin, Mengdi Wang, Yu-Xiang Wang, Yian Ma

UAI 2023 Private Prediction Strikes Back! Private Kernelized Nearest Neighbors with Individual Rényi Filter Yuqing Zhu, Xuandong Zhao, Chuan Guo, Yu-Xiang Wang

ICML 2023 Protecting Language Generation Models via Invisible Watermarking Xuandong Zhao, Yu-Xiang Wang, Lei Li

ICMLW 2023 Provable Robust Watermarking for AI-Generated Text Xuandong Zhao, Prabhanjan Vijendra Ananth, Lei Li, Yu-Xiang Wang

NeurIPSW 2023 Provable Robust Watermarking for AI-Generated Text Xuandong Zhao, Prabhanjan Vijendra Ananth, Lei Li, Yu-Xiang Wang

AISTATS 2023 Second Order Path Variationals in Non-Stationary Online Learning Dheeraj Baby, Yu-Xiang Wang

TMLR 2023 Smoothed Differential Privacy Ao Liu, Yu-Xiang Wang, Lirong Xia

ICMLW 2023 Why Quantization Improves Generalization: NTK of Binary Weight Neural Network Kaiqi Zhang, Ming Yin, Yu-Xiang Wang

ICLRW 2023 Zero Redundancy Distributed Learning with Differential Privacy Zhiqi Bu, Justin Chiu, Ruixuan Liu, Yu-Xiang Wang, Sheng Zha, George Karypis

AISTATS 2022 Adaptive Private-K-Selection with Adaptive K and Application to Multi-Label PATE Yuqing Zhu, Yu-Xiang Wang

AISTATS 2022 Non-Stationary Online Learning with Memory and Non-Stochastic Control Peng Zhao, Yu-Xiang Wang, Zhi-Hua Zhou

AISTATS 2022 Optimal Accounting of Differential Privacy via Characteristic Function Yuqing Zhu, Jinshuo Dong, Yu-Xiang Wang

AISTATS 2022 Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond Dheeraj Baby, Yu-Xiang Wang

AISTATS 2022 Towards Agnostic Feature-Based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise Jianyu Xu, Yu-Xiang Wang

NeurIPSW 2022 Differentially Private Bias-Term Only Fine-Tuning of Foundation Models Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis

NeurIPSW 2022 Differentially Private Gradient Boosting on Linear Learners for Tabular Data Saeyoung Rho, Cedric Archambeau, Sergul Aydore, Beyza Ermis, Michael Kearns, Aaron Roth, Shuai Tang, Yu-Xiang Wang, Steven Wu

NeurIPS 2022 Differentially Private Linear Sketches: Efficient Implementations and Applications Fuheng Zhao, Dan Qiao, Rachel Redberg, Divyakant Agrawal, Amr El Abbadi, Yu-Xiang Wang

JAIR 2022 Doubly Robust Crowdsourcing Chong Liu, Yu-Xiang Wang

CVPR 2022 Mixed Differential Privacy in Computer Vision Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto

NeurIPSW 2022 Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation Dan Qiao, Yu-Xiang Wang

ICLR 2022 Near-Optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism Ming Yin, Yaqi Duan, Mengdi Wang, Yu-Xiang Wang

NeurIPSW 2022 Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data Sunil Madhow, Dan Qiao, Yu-Xiang Wang

NeurIPSW 2022 Offline Reinforcement Learning with Closed-Form Policy Improvement Operators Jiachen Li, Edwin Zhang, Ming Yin, Qinxun Bai, Yu-Xiang Wang, William Yang Wang

UAI 2022 Offline Stochastic Shortest Path: Learning, Evaluation and Towards Optimality Ming Yin, Wenjing Chen, Mengdi Wang, Yu-Xiang Wang

NeurIPS 2022 Optimal Dynamic Regret in LQR Control Dheeraj Baby, Yu-Xiang Wang

ICML 2022 Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost Dan Qiao, Ming Yin, Ming Min, Yu-Xiang Wang

NeurIPS 2022 SeqPATE: Differentially Private Text Generation via Knowledge Distillation Zhiliang Tian, Yingxiu Zhao, Ziyue Huang, Yu-Xiang Wang, Nevin L. Zhang, He He

NeurIPSW 2022 Voting-Based Approaches for Differentially Private Federated Learning Yuqing Zhu, Xiang Yu, Yi-Hsuan Tsai, Francesco Pittaluga, Masoud Faraki, Manmohan Chandraker, Yu-Xiang Wang

AISTATS 2021 An Optimal Reduction of TV-Denoising to Adaptive Online Learning Dheeraj Baby, Xuandong Zhao, Yu-Xiang Wang

AISTATS 2021 Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning Ming Yin, Yu Bai, Yu-Xiang Wang

AISTATS 2021 Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang

NeurIPS 2021 Logarithmic Regret in Feature-Based Dynamic Pricing Jianyu Xu, Yu-Xiang Wang

NeurIPS 2021 Near-Optimal Offline Reinforcement Learning via Double Variance Reduction Ming Yin, Yu Bai, Yu-Xiang Wang

COLT 2021 Optimal Dynamic Regret in Exp-Concave Online Learning Dheeraj Baby, Yu-Xiang Wang

NeurIPS 2021 Optimal Uniform OPE and Model-Based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings Ming Yin, Yu-Xiang Wang

NeurIPS 2021 Privately Publishable Per-Instance Privacy Rachel Redberg, Yu-Xiang Wang

JMLR 2021 Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang

NeurIPS 2021 Towards Instance-Optimal Offline Reinforcement Learning with Pessimism Ming Yin, Yu-Xiang Wang

NeurIPS 2020 Adaptive Online Estimation of Piecewise Polynomial Trends Dheeraj Baby, Yu-Xiang Wang

ICML 2020 An End-to-End Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm Chris Decarolis, Mukul Ram, Seyed Esmaeili, Yu-Xiang Wang, Furong Huang

AISTATS 2020 Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning Ming Yin, Yu-Xiang Wang

NeurIPS 2020 Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, Geoffrey J. Gordon

NeurIPS 2020 Improving Sparse Vector Technique with Renyi Differential Privacy Yuqing Zhu, Yu-Xiang Wang

AISTATS 2019 A Higher-Order Kolmogorov-Smirnov Test Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani

NeurIPS 2019 Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, Xifeng Yan

AISTATS 2019 Imitation-Regularized Offline Learning Yifei Ma, Yu-Xiang Wang, Balakrishnan Narayanaswamy

NeurIPS 2019 Online Forecasting of Total-Variation-Bounded Sequences Dheeraj Baby, Yu-Xiang Wang

ICML 2019 Poission Subsampled Rényi Differential Privacy Yuqing Zhu, Yu-Xiang Wang

NeurIPS 2019 Provably Efficient Q-Learning with Low Switching Cost Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang

ICLR 2019 ProxQuant: Quantized Neural Networks via Proximal Operators Yu Bai, Yu-Xiang Wang, Edo Liberty

AISTATS 2019 Subsampled Renyi Differential Privacy and Analytical Moments Accountant Yu-Xiang Wang, Borja Balle, Shiva Prasad Kasiviswanathan

NeurIPS 2019 Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling Tengyang Xie, Yifei Ma, Yu-Xiang Wang

ICML 2018 Detecting and Correcting for Label Shift with Black Box Predictors Zachary Lipton, Yu-Xiang Wang, Alexander Smola

ICML 2018 Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising Borja Balle, Yu-Xiang Wang

UAI 2018 Revisiting Differentially Private Linear Regression: Optimal and Adaptive Prediction & Estimation in Unbounded Domain Yu-Xiang Wang

ICML 2018 signSGD: Compressed Optimisation for Non-Convex Problems Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Animashree Anandkumar

AISTATS 2017 Attributing Hacks Ziqi Liu, Alexander J. Smola, Kyle Soska, Yu-Xiang Wang, Qinghua Zheng

NeurIPS 2017 Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods Veeranjaneyulu Sadhanala, Yu-Xiang Wang, James L Sharpnack, Ryan J Tibshirani

ICML 2017 Optimal and Adaptive Off-Policy Evaluation in Contextual Bandits Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudı́k

AISTATS 2016 Graph Connectivity in Noisy Sparse Subspace Clustering Yining Wang, Yu-Xiang Wang, Aarti Singh

AISTATS 2016 Graph Sparsification Approaches for Laplacian Smoothing Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J. Tibshirani

JMLR 2016 Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg

JMLR 2016 Noisy Sparse Subspace Clustering Yu-Xiang Wang, Huan Xu

ICML 2016 Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric Xing

NeurIPS 2016 Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J Tibshirani

JMLR 2016 Trend Filtering on Graphs Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani

ICML 2015 A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-Reduced Data Yining Wang, Yu-Xiang Wang, Aarti Singh

NeurIPS 2015 Differentially Private Subspace Clustering Yining Wang, Yu-Xiang Wang, Aarti Singh

ICML 2015 Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo Yu-Xiang Wang, Stephen Fienberg, Alex Smola

AISTATS 2015 Trend Filtering on Graphs Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani

ICML 2014 The Falling Factorial Basis and Its Statistical Applications Yu-Xiang Wang, Alex Smola, Ryan Tibshirani

ICML 2013 Noisy Sparse Subspace Clustering Yu-Xiang Wang, Huan Xu

NeurIPS 2013 Provable Subspace Clustering: When LRR Meets SSC Yu-Xiang Wang, Huan Xu, Chenlei Leng

ICML 2012 Stability of Matrix Factorization for Collaborative Filtering Yu-Xiang Wang, Huan Xu