Real-Time Volume Caustics with Image-Based Photon Tracing. - Yu-Xiang Wang 003; Bin Wang; Li Chen (CAD/Graphics, 2011)

Stability of matrix factorization for collaborative filtering. - Yu-Xiang Wang 003; Huan Xu (ICML, 2012)

The Falling Factorial Basis and Its Statistical Applications. - Yu-Xiang Wang 003; Alexander J. Smola; Ryan J. Tibshirani (ICML, 2014)

Block-Sparse RPCA for Salient Motion Detection. - Zhi Gao; Loong-Fah Cheong; Yu-Xiang Wang 003 (IEEE Trans. Pattern Anal. Mach. Intell., 2014)

Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo. - Yu-Xiang Wang 003; Stephen E. Fienberg; Alexander J. Smola (ICML, 2015)

A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data. - Yining Wang 001; Yu-Xiang Wang 003; Aarti Singh (ICML, 2015)

Who Supported Obama in 2012?: Ecological Inference through Distribution Regression. - Seth R. Flaxman; Yu-Xiang Wang 003; Alexander J. Smola (KDD, 2015)

Differentially private subspace clustering. - Yining Wang 001; Yu-Xiang Wang 003; Aarti Singh (NIPS, 2015)

Fast Differentially Private Matrix Factorization. - Ziqi Liu; Yu-Xiang Wang 003; Alexander J. Smola (RecSys, 2015)

Clustering Consistent Sparse Subspace Clustering. - Yining Wang 001; Yu-Xiang Wang 003; Aarti Singh (CoRR, 2015)

Practical Matrix Completion and Corruption Recovery Using Proximal Alternating Robust Subspace Minimization. - Yu-Xiang Wang 003; Choon Meng Lee; Loong-Fah Cheong; Kim-Chuan Toh (Int. J. Comput. Vis., 2015)

Graph Sparsification Approaches for Laplacian Smoothing. - Veeranjaneyulu Sadhanala; Yu-Xiang Wang 003; Ryan J. Tibshirani (AISTATS, 2016)

Graph Connectivity in Noisy Sparse Subspace Clustering. - Yining Wang 001; Yu-Xiang Wang 003; Aarti Singh (AISTATS, 2016)

Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms. - Yu-Xiang Wang 003; Veeranjaneyulu Sadhanala; Wei Dai 003; Willie Neiswanger; Suvrit Sra; Eric P. Xing (ICML, 2016)

Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers. - Veeranjaneyulu Sadhanala; Yu-Xiang Wang 003; Ryan J. Tibshirani (NIPS, 2016)

On-Average KL-Privacy and Its Equivalence to Generalization for Max-Entropy Mechanisms. - Yu-Xiang Wang 003; Jing Lei 003; Stephen E. Fienberg (PSD, 2016)

DiFacto: Distributed Factorization Machines. - Mu Li 003; Ziqi Liu; Alexander J. Smola; Yu-Xiang Wang 003 (WSDM, 2016)

A Minimax Theory for Adaptive Data Analysis. - Yu-Xiang Wang 003; Jing Lei 003; Stephen E. Fienberg (CoRR, 2016)

Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle. - Yu-Xiang Wang 003; Jing Lei 003; Stephen E. Fienberg (J. Mach. Learn. Res., 2016)

Trend Filtering on Graphs. - Yu-Xiang Wang 003; James Sharpnack; Alexander J. Smola; Ryan J. Tibshirani (J. Mach. Learn. Res., 2016)

Noisy Sparse Subspace Clustering. - Yu-Xiang Wang 003; Huan Xu (J. Mach. Learn. Res., 2016)

Attributing Hacks. - Ziqi Liu; Alexander J. Smola; Kyle Soska; Yu-Xiang Wang 003; Qinghua Zheng (AISTATS, 2017)

Optimal and Adaptive Off-policy Evaluation in Contextual Bandits. - Yu-Xiang Wang 003; Alekh Agarwal; Miroslav Dudík (ICML, 2017)

Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods. - Veeranjaneyulu Sadhanala; Yu-Xiang Wang 003; James Sharpnack; Ryan J. Tibshirani (NIPS, 2017)

Per-instance Differential Privacy and the Adaptivity of Posterior Sampling in Linear and Ridge regression. - Yu-Xiang Wang 003 (CoRR, 2017)

Non-stationary Stochastic Optimization with Local Spatial and Temporal Changes. - Xi Chen 010; Yining Wang 001; Yu-Xiang Wang 003 (CoRR, 2017)

New Paradigms and Optimality Guarantees in Statistical Learning and Estimation. - Yu-Xiang Wang 003 (2017)

Compression by the signs: distributed learning is a two-way street. - Jeremy Bernstein; Yu-Xiang Wang 003; Kamyar Azizzadenesheli; Anima Anandkumar (ICLR (Workshop), 2018)

Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising. - Borja Balle; Yu-Xiang Wang 003 (ICML, 2018)

SIGNSGD: Compressed Optimisation for Non-Convex Problems. - Jeremy Bernstein; Yu-Xiang Wang 003; Kamyar Azizzadenesheli; Animashree Anandkumar (ICML, 2018)

Detecting and Correcting for Label Shift with Black Box Predictors. - Zachary C. Lipton; Yu-Xiang Wang 003; Alexander J. Smola (ICML, 2018)

Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain. - Yu-Xiang Wang 003 (UAI, 2018)

Imitation-Regularized Offline Learning. - Yifei Ma; Yu-Xiang Wang 003; Balakrishnan Narayanaswamy (AISTATS, 2019)

A Higher-Order Kolmogorov-Smirnov Test. - Veeranjaneyulu Sadhanala; Yu-Xiang Wang 003; Aaditya Ramdas; Ryan J. Tibshirani (AISTATS, 2019)

ProxQuant: Quantized Neural Networks via Proximal Operators. - Yu Bai 017; Yu-Xiang Wang 003; Edo Liberty (ICLR (Poster), 2019)

Poission Subsampled Rényi Differential Privacy. - Yuqing Zhu 005; Yu-Xiang Wang 003 (ICML, 2019)

Online Forecasting of Total-Variation-bounded Sequences. - Dheeraj Baby; Yu-Xiang Wang 003 (NeurIPS, 2019)

Provably Efficient Q-Learning with Low Switching Cost. - Yu Bai 017; Tengyang Xie; Nan Jiang 008; Yu-Xiang Wang 003 (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 003; Xifeng Yan (NeurIPS, 2019)

Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling. - Tengyang Xie; Yifei Ma; Yu-Xiang Wang 003 (NeurIPS, 2019)

Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling. - Tengyang Xie; Yifei Ma; Yu-Xiang Wang 003 (CoRR, 2019)

Technical Note - Nonstationary Stochastic Optimization Under Lp, q-Variation Measures. - Xi Chen 010; Yining Wang 001; Yu-Xiang Wang 003 (Oper. Res., 2019)

Per-instance Differential Privacy. - Yu-Xiang Wang 003 (J. Priv. Confidentiality, 2019)

A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data. - Yining Wang 001; Yu-Xiang Wang 003; Aarti Singh (IEEE Trans. Inf. Theory, 2019)

Provable Subspace Clustering: When LRR Meets SSC. - Yu-Xiang Wang 003; Huan Xu; Chenlei Leng (IEEE Trans. Inf. Theory, 2019)

Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning. - Ming Yin 003; Yu-Xiang Wang 003 (AISTATS, 2020)

Private-kNN: Practical Differential Privacy for Computer Vision. - Yuqing Zhu 005; Xiang Yu 002; Manmohan Chandraker; Yu-Xiang Wang 003 (CVPR, 2020)

An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm. - Chris Decarolis; Mukul Ram; Seyed Esmaeili; Yu-Xiang Wang 003; Furong Huang (ICML, 2020)

Improving Sparse Vector Technique with Renyi Differential Privacy. - Yuqing Zhu 005; Yu-Xiang Wang 003 (NeurIPS, 2020)

Adaptive Online Estimation of Piecewise Polynomial Trends. - Dheeraj Baby; Yu-Xiang Wang 003 (NeurIPS, 2020)

Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift. - Remi Tachet des Combes; Han Zhao 002; Yu-Xiang Wang 003; Geoffrey J. Gordon (NeurIPS, 2020)

Near Optimal Provable Uniform Convergence in Off-Policy Evaluation for Reinforcement Learning. - Ming Yin 003; Yu Bai 017; Yu-Xiang Wang 003 (CoRR, 2020)

Voting-based Approaches For Differentially Private Federated Learning. - Yuqing Zhu 005; Xiang Yu 002; Yi-Hsuan Tsai; Francesco Pittaluga; Masoud Faraki; Manmohan Chandraker; Yu-Xiang Wang 003 (CoRR, 2020)

Subsampled Rényi Differential Privacy and Analytical Moments Accountant. - Yu-Xiang Wang 003; Borja Balle; Shiva Prasad Kasiviswanathan (J. Priv. Confidentiality, 2020)

Patch-Based Image Hallucination for Super Resolution With Detail Reconstruction From Similar Sample Images. - Chieh-Chi Kao; Yu-Xiang Wang 003; Jonathan Waltman; Pradeep Sen (IEEE Trans. Multim., 2020)

An Optimal Reduction of TV-Denoising to Adaptive Online Learning. - Dheeraj Baby; Xuandong Zhao; Yu-Xiang Wang 003 (AISTATS, 2021)

Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning. - Ming Yin 003; Yu Bai 017; Yu-Xiang Wang 003 (AISTATS, 2021)

Optimal Dynamic Regret in Exp-Concave Online Learning. - Dheeraj Baby; Yu-Xiang Wang 003 (COLT, 2021)

Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability. - Hojjat Aghakhani; Dongyu Meng; Yu-Xiang Wang 003; Christopher Kruegel; Giovanni Vigna (EuroS&P, 2021)

Privately Publishable Per-instance Privacy. - Rachel Redberg; Yu-Xiang Wang 003 (NeurIPS, 2021)

Logarithmic Regret in Feature-based Dynamic Pricing. - Jianyu Xu; Yu-Xiang Wang 003 (NeurIPS, 2021)

Near-Optimal Offline Reinforcement Learning via Double Variance Reduction. - Ming Yin 003; Yu Bai 017; Yu-Xiang Wang 003 (NeurIPS, 2021)

Towards Instance-Optimal Offline Reinforcement Learning with Pessimism. - Ming Yin 003; Yu-Xiang Wang 003 (NeurIPS, 2021)

Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings. - Ming Yin 003; Yu-Xiang Wang 003 (NeurIPS, 2021)

Inter-Series Attention Model for COVID-19 Forecasting. - Xiaoyong Jin; Yu-Xiang Wang 003; Xifeng Yan (SDM, 2021)

Multivariate Trend Filtering for Lattice Data. - Veeranjaneyulu Sadhanala; Yu-Xiang Wang 003; Addison J. Hu; Ryan J. Tibshirani (CoRR, 2021)

Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. - Chong Liu 007; Yuqing Zhu 005; Kamalika Chaudhuri; Yu-Xiang Wang 003 (J. Mach. Learn. Res., 2021)

Optimal Accounting of Differential Privacy via Characteristic Function. - Yuqing Zhu 005; Jinshuo Dong; Yu-Xiang Wang 003 (AISTATS, 2022)

Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE. - Yuqing Zhu 005; Yu-Xiang Wang 003 (AISTATS, 2022)

Non-stationary Online Learning with Memory and Non-stochastic Control. - Peng Zhao 006; Yu-Xiang Wang 003; Zhi-Hua Zhou (AISTATS, 2022)

Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond. - Dheeraj Baby; Yu-Xiang Wang 003 (AISTATS, 2022)

Towards Agnostic Feature-based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise. - Jianyu Xu; Yu-Xiang Wang 003 (AISTATS, 2022)

Mixed Differential Privacy in Computer Vision. - Aditya Golatkar; Alessandro Achille; Yu-Xiang Wang 003; Aaron Roth 001; Michael Kearns; Stefano Soatto (CVPR, 2022)

Distillation-Resistant Watermarking for Model Protection in NLP. - Xuandong Zhao; Lei Li 005; Yu-Xiang Wang 003 (EMNLP (Findings), 2022)

Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism. - Ming Yin 003; Yaqi Duan; Mengdi Wang; Yu-Xiang Wang 003 (ICLR, 2022)

Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost. - Dan Qiao 002; Ming Yin 003; Ming Min; Yu-Xiang Wang 003 (ICML, 2022)

Provably Confidential Language Modelling. - Xuandong Zhao; Lei Li 005; Yu-Xiang Wang 003 (NAACL-HLT, 2022)

Optimal Dynamic Regret in LQR Control. - Dheeraj Baby; Yu-Xiang Wang 003 (NeurIPS, 2022)

SeqPATE: Differentially Private Text Generation via Knowledge Distillation. - Zhiliang Tian; Yingxiu Zhao; Ziyue Huang; Yu-Xiang Wang 003; Nevin L. Zhang; He He (NeurIPS, 2022)

Differentially Private Linear Sketches: Efficient Implementations and Applications. - Fuheng Zhao; Dan Qiao 002; Rachel Redberg; Divyakant Agrawal; Amr El Abbadi; Yu-Xiang Wang 003 (NeurIPS, 2022)

Offline stochastic shortest path: Learning, evaluation and towards optimality. - Ming Yin 003; Wenjing Chen; Mengdi Wang; Yu-Xiang Wang 003 (UAI, 2022)

Towards Differential Relational Privacy and its use in Question Answering. - Simone Bombari; Alessandro Achille; Zijian Wang 002; Yu-Xiang Wang 003; Yusheng Xie; Kunwar Yashraj Singh; Srikar Appalaraju; Vijay Mahadevan; Stefano Soatto (CoRR, 2022)

Why Quantization Improves Generalization: NTK of Binary Weight Neural Networks. - Kaiqi Zhang 002; Ming Yin 003; Yu-Xiang Wang 003 (CoRR, 2022)

Differentially Private Bias-Term only Fine-tuning of Foundation Models. - Zhiqi Bu; Yu-Xiang Wang 003; Sheng Zha; George Karypis (CoRR, 2022)

Doubly Robust Crowdsourcing. - Chong Liu 007; Yu-Xiang Wang 003 (J. Artif. Intell. Res., 2022)

Near-Optimal Differentially Private Reinforcement Learning. - Dan Qiao 002; Yu-Xiang Wang 003 (AISTATS, 2023)

Second Order Path Variationals in Non-Stationary Online Learning. - Dheeraj Baby; Yu-Xiang Wang 003 (AISTATS, 2023)

Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy. - Rachel Redberg; Yuqing Zhu 005; Yu-Xiang Wang 003 (AISTATS, 2023)

Doubly Fair Dynamic Pricing. - Jianyu Xu; Dan Qiao 002; Yu-Xiang Wang 003 (AISTATS, 2023)

Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation. - Dan Qiao 002; Yu-Xiang Wang 003 (ICLR, 2023)

Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient. - Ming Yin 003; Mengdi Wang; Yu-Xiang Wang 003 (ICLR, 2023)

Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive? - Kaiqi Zhang 002; Yu-Xiang Wang 003 (ICLR, 2023)

Differentially Private Optimization on Large Model at Small Cost. - Zhiqi Bu; Yu-Xiang Wang 003; Sheng Zha; George Karypis (ICML, 2023)

Non-stationary Reinforcement Learning under General Function Approximation. - Songtao Feng; Ming Yin 003; Ruiquan Huang; Yu-Xiang Wang 003; Jing Yang 002; Yingbin Liang (ICML, 2023)

Offline Reinforcement Learning with Closed-Form Policy Improvement Operators. - Jiachen Li; Edwin Zhang; Ming Yin 003; Qinxun Bai; Yu-Xiang Wang 003; William Yang Wang (ICML, 2023)

Global Optimization with Parametric Function Approximation. - Chong Liu 007; Yu-Xiang Wang 003 (ICML, 2023)

Protecting Language Generation Models via Invisible Watermarking. - Xuandong Zhao; Yu-Xiang Wang 003; Lei Li 005 (ICML, 2023)

Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms. - Dheeraj Baby; Saurabh Garg; Tzu-Ching Yen; Sivaraman Balakrishnan; Zachary C. Lipton; Yu-Xiang Wang 003 (NeurIPS, 2023)

Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger. - Zhiqi Bu; Yu-Xiang Wang 003; Sheng Zha; George Karypis (NeurIPS, 2023)

Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation. - Nikki Lijing Kuang; Ming Yin 003; Mengdi Wang; Yu-Xiang Wang 003; Yian Ma (NeurIPS, 2023)

Offline Reinforcement Learning with Differential Privacy. - Dan Qiao 002; Yu-Xiang Wang 003 (NeurIPS, 2023)

Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners. - Rachel Redberg; Antti Koskela; Yu-Xiang Wang 003 (NeurIPS, 2023)

A Privacy-Friendly Approach to Data Valuation. - Jiachen T. Wang; Yuqing Zhu 005; Yu-Xiang Wang 003; Ruoxi Jia 001; Prateek Mittal (NeurIPS, 2023)

Private Prediction Strikes Back! Private Kernelized Nearest Neighbors with Individual Rényi Filter. - Yuqing Zhu 005; Xuandong Zhao; Chuan Guo 001; Yu-Xiang Wang 003 (UAI, 2023)

No-Regret Linear Bandits beyond Realizability. - Chong Liu 007; Ming Yin 003; Yu-Xiang Wang 003 (UAI, 2023)

Logarithmic Switching Cost in Reinforcement Learning beyond Linear MDPs. - Dan Qiao 002; Ming Yin 003; Yu-Xiang Wang 003 (CoRR, 2023)

Challenges towards the Next Frontier in Privacy. - Rachel Cummings; Damien Desfontaines; David Evans 001; Roxana Geambasu; Matthew Jagielski; Yangsibo Huang; Peter Kairouz; Gautam Kamath 001; Sewoong Oh; Olga Ohrimenko; Nicolas Papernot; Ryan Rogers 002; et al. (CoRR, 2023)

Generative Autoencoders as Watermark Attackers: Analyses of Vulnerabilities and Threats. - Xuandong Zhao; Kexun Zhang; Yu-Xiang Wang 003; Lei Li 005 (CoRR, 2023)

Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data. - Sunil Madhow; Dan Xiao; Ming Yin 003; Yu-Xiang Wang 003 (CoRR, 2023)

Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks. - Kaiqi Zhang 002; Zixuan Zhang; Minshuo Chen; Mengdi Wang; Tuo Zhao; Yu-Xiang Wang 003 (CoRR, 2023)

Model-Free Algorithm with Improved Sample Efficiency for Zero-Sum Markov Games. - Songtao Feng; Ming Yin 003; Yu-Xiang Wang 003; Jing Yang 002; Yingbin Liang (CoRR, 2023)

Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation. - Jiachen T. Wang; Yuqing Zhu 005; Yu-Xiang Wang 003; Ruoxi Jia 001; Prateek Mittal (CoRR, 2023)

Coupling public and private gradient provably helps optimization. - Ruixuan Liu; Zhiqi Bu; Yu-Xiang Wang 003; Sheng Zha; George Karypis (CoRR, 2023)

Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy. - Yingyu Lin; Yian Ma; Yu-Xiang Wang 003; Rachel Redberg (CoRR, 2023)

On the accuracy and efficiency of group-wise clipping in differentially private optimization. - Zhiqi Bu; Ruixuan Liu; Yu-Xiang Wang 003; Sheng Zha; George Karypis (CoRR, 2023)

Non-stationary Online Learning with Memory and Non-stochastic Control. - Peng Zhao 006; Yu-Hu Yan; Yu-Xiang Wang 003; Zhi-Hua Zhou (J. Mach. Learn. Res., 2023)

Non-Stationary Contextual Pricing with Safety Constraints. - Dheeraj Baby; Jianyu Xu; Yu-Xiang Wang 003 (Trans. Mach. Learn. Res., 2023)

Smoothed Differential Privacy. - Ao Liu 001; Yu-Xiang Wang 003; Lirong Xia (Trans. Mach. Learn. Res., 2023)

CPR: Retrieval Augmented Generation for Copyright Protection. - Aditya Golatkar; Alessandro Achille; Luca Zancato; Yu-Xiang Wang 003; Ashwin Swaminathan; Stefano Soatto (CVPR, 2024)

Communication-Efficient Federated Non-Linear Bandit Optimization. - Chuanhao Li; Chong Liu 007; Yu-Xiang Wang 003 (ICLR, 2024)

Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy. - Yingyu Lin; Yian Ma; Yu-Xiang Wang 003; Rachel Redberg; Zhiqi Bu (ICLR, 2024)

Provable Robust Watermarking for AI-Generated Text. - Xuandong Zhao; Prabhanjan Vijendra Ananth; Lei Li 005; Yu-Xiang Wang 003 (ICLR, 2024)

Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints. - Dan Qiao 002; Yu-Xiang Wang 003 (ICML, 2024)

Differentially Private Bias-Term Fine-tuning of Foundation Models. - Zhiqi Bu; Yu-Xiang Wang 003; Sheng Zha; George Karypis (ICML, 2024)

Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games. - Songtao Feng; Ming Yin 003; Yu-Xiang Wang 003; Jing Yang 002; Yingbin Liang (ICML, 2024)

Privacy Profiles for Private Selection. - Antti Koskela; Rachel Redberg; Yu-Xiang Wang 003 (ICML, 2024)

Neural Collapse meets Differential Privacy: Curious behaviors of NoisyGD with Near-Perfect Representation Learning. - Chendi Wang; Yuqing Zhu 005; Weijie J. Su; Yu-Xiang Wang 003 (ICML, 2024)

Pricing with Contextual Elasticity and Heteroscedastic Valuation. - Jianyu Xu; Yu-Xiang Wang 003 (ICML, 2024)

Towards General Function Approximation in Nonstationary Reinforcement Learning. - Songtao Feng; Ming Yin 003; Ruiquan Huang; Yu-Xiang Wang 003; Jing Yang 002; Yingbin Liang (ISIT, 2024)

Differentially Private Reinforcement Learning with Self-Play. - Dan Qiao 002; Yu-Xiang Wang 003 (NeurIPS, 2024)

Stable Minima Cannot Overfit in Univariate ReLU Networks: Generalization by Large Step Sizes. - Dan Qiao 002; Kaiqi Zhang 002; Esha Singh; Daniel Soudry; Yu-Xiang Wang 003 (NeurIPS, 2024)

Online Feature Updates Improve Online (Generalized) Label Shift Adaptation. - Ruihan Wu; Siddhartha Datta; Yi Su; Dheeraj Baby; Yu-Xiang Wang 003; Kilian Q. Weinberger (NeurIPS, 2024)

Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks. - Zixuan Zhang; Kaiqi Zhang 002; Minshuo Chen; Yuma Takeda; Mengdi Wang; Tuo Zhao; Yu-Xiang Wang 003 (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 003; Lei Li 005 (NeurIPS, 2024)

Improved Differentially Private Regression via Gradient Boosting. - Shuai Tang; Sergül Aydöre; Michael Kearns; Saeyoung Rho; Aaron Roth 001; Yichen Wang; Yu-Xiang Wang 003; Zhiwei Steven Wu (SaTML, 2024)

Weak-to-Strong Jailbreaking on Large Language Models. - Xuandong Zhao; Xianjun Yang; Tianyu Pang; Chao Du; Lei Li 005; Yu-Xiang Wang 003; William Yang Wang (CoRR, 2024)

Permute-and-Flip: An optimally robust and watermarkable decoder for LLMs. - Xuandong Zhao; Lei Li 005; Yu-Xiang Wang 003 (CoRR, 2024)

Efficiently Identifying Watermarked Segments in Mixed-Source Texts. - Xuandong Zhao; Chenwen Liao; Yu-Xiang Wang 003; Lei Li 005 (CoRR, 2024)

Toward General Function Approximation in Nonstationary Reinforcement Learning. - Songtao Feng; Ming Yin 003; Ruiquan Huang; Yu-Xiang Wang 003; Jing Yang 002; Yingbin Liang (IEEE J. Sel. Areas Inf. Theory, 2024)

MoXCo: How I learned to stop exploring and love my local minima? - Esha Singh; Shoham Sabach; Yu-Xiang Wang 003 (CPAL, 2025)

Permute-and-Flip: An optimally stable and watermarkable decoder for LLMs. - Xuandong Zhao; Lei Li 005; Yu-Xiang Wang 003 (ICLR, 2025)

SoK: Watermarking for AI-Generated Content. - Xuandong Zhao; Sam Gunn; Miranda Christ; Jaiden Fairoze; Andres Fabrega; Nicholas Carlini; Sanjam Garg; Sanghyun Hong 001; Milad Nasr; Florian Tramèr; Somesh Jha; Lei Li 005; et al. (SP, 2025)

A Proximal Operator for Inducing 2:4-Sparsity. - Jonas M. Kübler; Yu-Xiang Wang 003; Shoham Sabach; Navid Ansari; Matthäus Kleindessner; Kailash Budhathoki; Volkan Cevher; George Karypis (Trans. Mach. Learn. Res., 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 003; George Karypis (CoRR, 2025)

Purifying Approximate Differential Privacy with Randomized Post-processing. - Yingyu Lin; Erchi Wang; Yi-An Ma; Yu-Xiang Wang 003 (CoRR, 2025)