| 2025 | ICLR | Permute-and-Flip: An optimally stable and watermarkable decoder for LLMs. | Xuandong Zhao, Lei Li, Yu-Xiang Wang |
| 2025 | ICML | Adaptive Estimation and Learning under Temporal Distribution Shift. | Dheeraj Baby, Yifei Tang, Hieu Duy Nguyen, Yu-Xiang Wang, Rohit Pyati |
| 2025 | ICML | 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 |
| 2025 | ICML | AKORN: Adaptive Knots generated Online for RegressioN splines. | Sunil Madhow, Dheeraj Baby, Yu-Xiang Wang |
| 2025 | ICML | Adapting to Linear Separable Subsets with Large-Margin in Differentially Private Learning. | Erchi Wang, Yuqing Zhu, Yu-Xiang Wang |
| 2025 | ICML | Weak-to-Strong Jailbreaking on Large Language Models. | Xuandong Zhao, Xianjun Yang, Tianyu Pang, Chao Du, Lei Li, Yu-Xiang Wang, William Yang Wang |
| 2025 | SP | SoK: Watermarking for AI-Generated Content. | Xuandong Zhao, Sam Gunn, Miranda Christ, Jaiden Fairoze, Andrs Fbrega, Nicholas Carlini, Sanjam Garg, Sanghyun Hong, Milad Nasr, Florian Tramr, Somesh Jha, Lei Li, Yu-Xiang Wang, Dawn Song |
| 2024 | ACL | Watermarking for Large Language Models. | Xuandong Zhao, Yu-Xiang Wang, Lei Li |
| 2024 | CVPR | CPR: Retrieval Augmented Generation for Copyright Protection. | Aditya Golatkar, Alessandro Achille, Luca Zancato, Yu-Xiang Wang, Ashwin Swaminathan, Stefano Soatto |
| 2024 | ICLR | Communication-Efficient Federated Non-Linear Bandit Optimization. | Chuanhao Li, Chong Liu, Yu-Xiang Wang |
| 2024 | ICLR | Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy. | Yingyu Lin, Yian Ma, Yu-Xiang Wang, Rachel Redberg, Zhiqi Bu |
| 2024 | ICLR | Provable Robust Watermarking for AI-Generated Text. | Xuandong Zhao, Prabhanjan Vijendra Ananth, Lei Li, Yu-Xiang Wang |
| 2024 | ICML | Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints. | Dan Qiao, Yu-Xiang Wang |
| 2024 | ICML | Differentially Private Bias-Term Fine-tuning of Foundation Models. | Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis |
| 2024 | ICML | Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games. | Songtao Feng, Ming Yin, Yu-Xiang Wang, Jing Yang, Yingbin Liang |
| 2024 | ICML | Privacy Profiles for Private Selection. | Antti Koskela, Rachel Redberg, Yu-Xiang Wang |
| 2024 | ICML | Neural Collapse meets Differential Privacy: Curious behaviors of NoisyGD with Near-Perfect Representation Learning. | Chendi Wang, Yuqing Zhu, Weijie J. Su, Yu-Xiang Wang |
| 2024 | ICML | Pricing with Contextual Elasticity and Heteroscedastic Valuation. | Jianyu Xu, Yu-Xiang Wang |
| 2024 | ISIT | Towards General Function Approximation in Nonstationary Reinforcement Learning. | Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang |
| 2023 | AISTATS | Near-Optimal Differentially Private Reinforcement Learning. | Dan Qiao, Yu-Xiang Wang |
| 2023 | AISTATS | Second Order Path Variationals in Non-Stationary Online Learning. | Dheeraj Baby, Yu-Xiang Wang |
| 2023 | AISTATS | Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy. | Rachel Redberg, Yuqing Zhu, Yu-Xiang Wang |
| 2023 | AISTATS | Doubly Fair Dynamic Pricing. | Jianyu Xu, Dan Qiao, Yu-Xiang Wang |
| 2023 | ICLR | Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation. | Dan Qiao, Yu-Xiang Wang |
| 2023 | ICLR | Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient. | Ming Yin, Mengdi Wang, Yu-Xiang Wang |
| 2023 | ICLR | Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive? | Kaiqi Zhang, Yu-Xiang Wang |
| 2023 | ICML | Differentially Private Optimization on Large Model at Small Cost. | Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis |
| 2023 | ICML | Non-stationary Reinforcement Learning under General Function Approximation. | Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang |
| 2023 | ICML | Global Optimization with Parametric Function Approximation. | Chong Liu, Yu-Xiang Wang |
| 2023 | ICML | Offline Reinforcement Learning with Closed-Form Policy Improvement Operators. | Jiachen Li, Edwin Zhang, Ming Yin, Qinxun Bai, Yu-Xiang Wang, William Yang Wang |
| 2023 | ICML | Protecting Language Generation Models via Invisible Watermarking. | Xuandong Zhao, Yu-Xiang Wang, Lei Li |
| 2023 | UAI | Private Prediction Strikes Back! Private Kernelized Nearest Neighbors with Individual Rnyi Filter. | Yuqing Zhu, Xuandong Zhao, Chuan Guo, Yu-Xiang Wang |
| 2023 | UAI | No-Regret Linear Bandits beyond Realizability. | Chong Liu, Ming Yin, Yu-Xiang Wang |
| 2022 | AISTATS | Optimal Accounting of Differential Privacy via Characteristic Function. | Yuqing Zhu, Jinshuo Dong, Yu-Xiang Wang |
| 2022 | AISTATS | Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE. | Yuqing Zhu, Yu-Xiang Wang |
| 2022 | AISTATS | Non-stationary Online Learning with Memory and Non-stochastic Control. | Peng Zhao, Yu-Xiang Wang, Zhi-Hua Zhou |
| 2022 | AISTATS | Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond. | Dheeraj Baby, Yu-Xiang Wang |
| 2022 | AISTATS | Towards Agnostic Feature-based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise. | Jianyu Xu, Yu-Xiang Wang |
| 2022 | CVPR | Mixed Differential Privacy in Computer Vision. | Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto |
| 2022 | EMNLP | Distillation-Resistant Watermarking for Model Protection in NLP. | Xuandong Zhao, Lei Li, Yu-Xiang Wang |
| 2022 | ICLR | Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism. | Ming Yin, Yaqi Duan, Mengdi Wang, Yu-Xiang Wang |
| 2022 | ICML | Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost. | Dan Qiao, Ming Yin, Ming Min, Yu-Xiang Wang |
| 2022 | NAACL | Provably Confidential Language Modelling. | Xuandong Zhao, Lei Li, Yu-Xiang Wang |
| 2022 | UAI | Offline stochastic shortest path: Learning, evaluation and towards optimality. | Ming Yin, Wenjing Chen, Mengdi Wang, Yu-Xiang Wang |
| 2021 | AISTATS | An Optimal Reduction of TV-Denoising to Adaptive Online Learning. | Dheeraj Baby, Xuandong Zhao, Yu-Xiang Wang |
| 2021 | AISTATS | Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. | Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang |
| 2021 | AISTATS | Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning. | Ming Yin, Yu Bai, Yu-Xiang Wang |
| 2021 | COLT | Optimal Dynamic Regret in Exp-Concave Online Learning. | Dheeraj Baby, Yu-Xiang Wang |
| 2021 | SDM | Inter-Series Attention Model for COVID-19 Forecasting. | Xiaoyong Jin, Yu-Xiang Wang, Xifeng Yan |
| 2020 | AISTATS | Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning. | Ming Yin, Yu-Xiang Wang |
| 2020 | CVPR | Private-kNN: Practical Differential Privacy for Computer Vision. | Yuqing Zhu, Xiang Yu, Manmohan Chandraker, Yu-Xiang Wang |
| 2020 | ICML | An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm. | Chris Decarolis, Mukul Ram, Seyed Esmaeili, Yu-Xiang Wang, Furong Huang |
| 2019 | AISTATS | Imitation-Regularized Offline Learning. | Yifei Ma, Yu-Xiang Wang, Balakrishnan Narayanaswamy |
| 2019 | AISTATS | A Higher-Order Kolmogorov-Smirnov Test. | Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani |
| 2019 | AISTATS | Subsampled Renyi Differential Privacy and Analytical Moments Accountant. | Yu-Xiang Wang, Borja Balle, Shiva Prasad Kasiviswanathan |
| 2019 | ICLR | ProxQuant: Quantized Neural Networks via Proximal Operators. | Yu Bai, Yu-Xiang Wang, Edo Liberty |
| 2019 | ICML | Poission Subsampled Rnyi Differential Privacy. | Yuqing Zhu, Yu-Xiang Wang |
| 2018 | ICLR | Compression by the signs: distributed learning is a two-way street. | Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Anima Anandkumar |
| 2018 | ICML | Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising. | Borja Balle, Yu-Xiang Wang |
| 2018 | ICML | SIGNSGD: Compressed Optimisation for Non-Convex Problems. | Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Animashree Anandkumar |
| 2018 | ICML | Detecting and Correcting for Label Shift with Black Box Predictors. | Zachary C. Lipton, Yu-Xiang Wang, Alexander J. Smola |
| 2018 | UAI | Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain. | Yu-Xiang Wang |
| 2017 | AISTATS | Attributing Hacks. | Ziqi Liu, Alexander J. Smola, Kyle Soska, Yu-Xiang Wang, Qinghua Zheng |
| 2017 | ICML | Optimal and Adaptive Off-policy Evaluation in Contextual Bandits. | Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudk |
| 2016 | AISTATS | Graph Sparsification Approaches for Laplacian Smoothing. | Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J. Tibshirani |
| 2016 | AISTATS | Graph Connectivity in Noisy Sparse Subspace Clustering. | Yining Wang, Yu-Xiang Wang, Aarti Singh |
| 2016 | ICML | Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms. | Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric P. Xing |
| 2016 | PSD | On-Average KL-Privacy and Its Equivalence to Generalization for Max-Entropy Mechanisms. | Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg |
| 2016 | SIGGRAPH | ThirdEye: a coaxial feature tracking system for stereoscopic video see-through augmented reality. | Yu-Xiang Wang, Yu-Ju Tsai, Yu-Hsuan Huang, Wan-Ling Yang, Tzu-Chieh Yu, Yu-Kai Chiu, Ming Ouhyoung |
| 2016 | WSDM | DiFacto: Distributed Factorization Machines. | Mu Li, Ziqi Liu, Alexander J. Smola, Yu-Xiang Wang |
| 2015 | AISTATS | Trend Filtering on Graphs. | Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani |
| 2015 | ICML | Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo. | Yu-Xiang Wang, Stephen E. Fienberg, Alexander J. Smola |
| 2015 | ICML | A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data. | Yining Wang, Yu-Xiang Wang, Aarti Singh |
| 2015 | KDD | Who Supported Obama in 2012?: Ecological Inference through Distribution Regression. | Seth R. Flaxman, Yu-Xiang Wang, Alexander J. Smola |
| 2015 | RecSys | Fast Differentially Private Matrix Factorization. | Ziqi Liu, Yu-Xiang Wang, Alexander J. Smola |
| 2015 | SIGGRAPH | Scope+: a stereoscopic video see-through augmented reality microscope. | Yu-Hsuan Huang, Tzu-Chieh Yu, Pei-Hsuan Tsai, Yu-Xiang Wang, Wan-ling Yang, Ming Ouhyoung |
| 2015 | UIST | Scope+: A Stereoscopic Video See-Through Augmented Reality Microscope. | Yu-Hsuan Huang, Tzu-Chieh Yu, Pei-Hsuan Tsai, Yu-Xiang Wang, Wan-ling Yang, Ming Ouhyoung |
| 2014 | ICML | The Falling Factorial Basis and Its Statistical Applications. | Yu-Xiang Wang, Alexander J. Smola, Ryan J. Tibshirani |
| 2013 | ICML | Noisy Sparse Subspace Clustering. | Yu-Xiang Wang, Huan Xu |
| 2012 | ICML | Stability of matrix factorization for collaborative filtering. | Yu-Xiang Wang, Huan Xu |