Yu-Xiang Wang

ORCID

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 133

Top Venues

Publications

Chendi Wang, Yuqing Zhu, Weijie J. Su, Yu-Xiang Wang
Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning. CoRR (2024)
Xuandong Zhao, Prabhanjan Vijendra Ananth, Lei Li, Yu-Xiang Wang
Provable Robust Watermarking for AI-Generated Text. ICLR (2024)
Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang
Toward General Function Approximation in Nonstationary Reinforcement Learning. IEEE J. Sel. Areas Inf. Theory 5 (2024)
Aditya Golatkar, Alessandro Achille, Luca Zancato, Yu-Xiang Wang, Ashwin Swaminathan, Stefano Soatto
CPR: Retrieval Augmented Generation for Copyright Protection. CoRR (2024)
Yingyu Lin, Yian Ma, Yu-Xiang Wang, Rachel Redberg, Zhiqi Bu
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy. ICLR (2024)
Shuai Tang, Sergül Aydöre, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Zhiwei Steven Wu
Improved Differentially Private Regression via Gradient Boosting. SaTML (2024)
Antti Koskela, Rachel Redberg, Yu-Xiang Wang
Privacy Profiles for Private Selection. CoRR (2024)
Rachel Redberg, Antti Koskela, Yu-Xiang Wang
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners. CoRR (2024)
Rachel Redberg, Yuqing Zhu, Yu-Xiang Wang
Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy. CoRR (2023)
Yingyu Lin, Yian Ma, Yu-Xiang Wang, Rachel Redberg
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy. CoRR (2023)
Jiachen T. Wang, Yuqing Zhu, Yu-Xiang Wang, Ruoxi Jia, Prateek Mittal
Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation. CoRR (2023)
Chuanhao Li, Chong Liu, Yu-Xiang Wang
Communication-Efficient Federated Non-Linear Bandit Optimization. CoRR (2023)
Jianyu Xu, Dan Qiao, Yu-Xiang Wang
Doubly Fair Dynamic Pricing. AISTATS (2023)
Zhiqi Bu, Ruixuan Liu, Yu-Xiang Wang, Sheng Zha, George Karypis
On the accuracy and efficiency of group-wise clipping in differentially private optimization. CoRR (2023)
Jiachen T. Wang, Yuqing Zhu, Yu-Xiang Wang, Ruoxi Jia, Prateek Mittal
A Privacy-Friendly Approach to Data Valuation. NeurIPS (2023)
Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen, Sivaraman Balakrishnan, Zachary C. Lipton, Yu-Xiang Wang
Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms. NeurIPS (2023)
Dheeraj Baby, Yu-Xiang Wang
Second Order Path Variationals in Non-Stationary Online Learning. AISTATS (2023)
Ruixuan Liu, Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis
Coupling public and private gradient provably helps optimization. CoRR (2023)
Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger. NeurIPS (2023)
Nikki Lijing Kuang, Ming Yin, Mengdi Wang, Yu-Xiang Wang, Yi-An Ma
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation. CoRR (2023)
Rachel Redberg, Yuqing Zhu, Yu-Xiang Wang
Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy. AISTATS (2023)
Nikki Lijing Kuang, Ming Yin, Mengdi Wang, Yu-Xiang Wang, Yian Ma
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation. NeurIPS (2023)
Dan Qiao, Yu-Xiang Wang
Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation. ICLR (2023)
Dan Qiao, Yu-Xiang Wang
Near-Optimal Differentially Private Reinforcement Learning. AISTATS (2023)
Rachel Redberg, Antti Koskela, Yu-Xiang Wang
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners. NeurIPS (2023)
Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen, Sivaraman Balakrishnan, Zachary Chase Lipton, Yu-Xiang Wang
Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms. CoRR (2023)
Dheeraj Baby, Yu-Xiang Wang
Second Order Path Variationals in Non-Stationary Online Learning. CoRR (2022)
Peng Zhao, Yu-Xiang Wang, Zhi-Hua Zhou
Non-stationary Online Learning with Memory and Non-stochastic Control. AISTATS (2022)
Fuheng Zhao, Dan Qiao, Rachel Redberg, Divyakant Agrawal, Amr El Abbadi, Yu-Xiang Wang
Differentially Private Linear Sketches: Efficient Implementations and Applications. CoRR (2022)
Ming Yin, Yaqi Duan, Mengdi Wang, Yu-Xiang Wang
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism. ICLR (2022)
Dan Qiao, Yu-Xiang Wang
Offline Reinforcement Learning with Differential Privacy. CoRR (2022)
Xuandong Zhao, Lei Li, Yu-Xiang Wang
Distillation-Resistant Watermarking for Model Protection in NLP. CoRR (2022)
Fuheng Zhao, Dan Qiao, Rachel Redberg, Divyakant Agrawal, Amr El Abbadi, Yu-Xiang Wang
Differentially Private Linear Sketches: Efficient Implementations and Applications. NeurIPS (2022)
Chong Liu, Yu-Xiang Wang
Global Optimization with Parametric Function Approximation. CoRR (2022)
Dan Qiao, Ming Yin, Ming Min, Yu-Xiang Wang
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost. ICML (2022)
Dheeraj Baby, Yu-Xiang Wang
Optimal Dynamic Regret in LQR Control. NeurIPS (2022)
Ming Yin, Wenjing Chen, Mengdi Wang, Yu-Xiang Wang
Offline stochastic shortest path: Learning, evaluation and towards optimality. UAI (2022)
Ming Yin, Mengdi Wang, Yu-Xiang Wang
Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient. CoRR (2022)
Dan Qiao, Ming Yin, Ming Min, Yu-Xiang Wang
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost. CoRR (2022)
Xuandong Zhao, Lei Li, Yu-Xiang Wang
Provably Confidential Language Modelling. NAACL-HLT (2022)
Dan Qiao, Yu-Xiang Wang
Near-Optimal Differentially Private Reinforcement Learning. CoRR (2022)
Ming Yin, Yaqi Duan, Mengdi Wang, Yu-Xiang Wang
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism. CoRR (2022)
Dheeraj Baby, Yu-Xiang Wang
Optimal Dynamic Regret in LQR Control. CoRR (2022)
Dheeraj Baby, Yu-Xiang Wang
Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond. CoRR (2022)
Xuandong Zhao, Lei Li, Yu-Xiang Wang
Provably Confidential Language Modelling. CoRR (2022)
Dheeraj Baby, Yu-Xiang Wang
Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond. AISTATS (2022)
Ming Yin, Wenjing Chen, Mengdi Wang, Yu-Xiang Wang
Offline Stochastic Shortest Path: Learning, Evaluation and Towards Optimality. CoRR (2022)
Hojjat Aghakhani, Dongyu Meng, Yu-Xiang Wang, Christopher Kruegel, Giovanni Vigna
Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability. EuroS&P (2021)
Ming Yin, Yu Bai, Yu-Xiang Wang
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction. CoRR (2021)
Ming Yin, Yu Bai, Yu-Xiang Wang
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction. NeurIPS (2021)
Ming Yin, Yu-Xiang Wang
Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings. CoRR (2021)
Dheeraj Baby, Xuandong Zhao, Yu-Xiang Wang
An Optimal Reduction of TV-Denoising to Adaptive Online Learning. CoRR (2021)
Xiaoyong Jin, Yu-Xiang Wang, Xifeng Yan
Inter-Series Attention Model for COVID-19 Forecasting. SDM (2021)
Peng Zhao, Yu-Xiang Wang, Zhi-Hua Zhou
Non-stationary Online Learning with Memory and Non-stochastic Control. CoRR (2021)
Dheeraj Baby, Yu-Xiang Wang
Optimal Dynamic Regret in Exp-Concave Online Learning. COLT (2021)
Rachel Redberg, Yu-Xiang Wang
Privately Publishable Per-instance Privacy. NeurIPS (2021)
Jianyu Xu, Yu-Xiang Wang
Logarithmic Regret in Feature-based Dynamic Pricing. NeurIPS (2021)
Yuqing Zhu, Jinshuo Dong, Yu-Xiang Wang
Optimal Accounting of Differential Privacy via Characteristic Function. CoRR (2021)
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Addison J. Hu, Ryan J. Tibshirani
Multivariate Trend Filtering for Lattice Data. CoRR (2021)
Ming Yin, Yu-Xiang Wang
Towards Instance-Optimal Offline Reinforcement Learning with Pessimism. NeurIPS (2021)
Ming Yin, Yu Bai, Yu-Xiang Wang
Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning. AISTATS (2021)
Ming Yin, Yu-Xiang Wang
Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings. NeurIPS (2021)
Ming Yin, Yu-Xiang Wang
Towards Instance-Optimal Offline Reinforcement Learning with Pessimism. CoRR (2021)
Dheeraj Baby, Xuandong Zhao, Yu-Xiang Wang
An Optimal Reduction of TV-Denoising to Adaptive Online Learning. AISTATS (2021)
Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. AISTATS (2021)
Jianyu Xu, Yu-Xiang Wang
Logarithmic Regret in Feature-based Dynamic Pricing. CoRR (2021)
Dheeraj Baby, Yu-Xiang Wang
Optimal Dynamic Regret in Exp-Concave Online Learning. CoRR (2021)
Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, Geoffrey J. Gordon
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift. CoRR (2020)
Ming Yin, Yu-Xiang Wang
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning. CoRR (2020)
Chieh-Chi Kao, Yu-Xiang Wang, Jonathan Waltman, Pradeep Sen
Patch-Based Image Hallucination for Super Resolution With Detail Reconstruction From Similar Sample Images. IEEE Trans. Multim. 22 (5) (2020)
Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. CoRR (2020)
Yuqing Zhu, Xiang Yu, Manmohan Chandraker, Yu-Xiang Wang
Private-kNN: Practical Differential Privacy for Computer Vision. CVPR (2020)
Dheeraj Baby, Yu-Xiang Wang
Adaptive Online Estimation of Piecewise Polynomial Trends. CoRR (2020)
Yu-Xiang Wang, Borja Balle, Shiva Prasad Kasiviswanathan
Subsampled Rényi Differential Privacy and Analytical Moments Accountant. J. Priv. Confidentiality 10 (2) (2020)
Chris Decarolis, Mukul Ram, Seyed Esmaeili, Yu-Xiang Wang, Furong Huang
An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm. ICML (2020)
Yuqing Zhu, Yu-Xiang Wang
Improving Sparse Vector Technique with Renyi Differential Privacy. NeurIPS (2020)
Dheeraj Baby, Yu-Xiang Wang
Adaptive Online Estimation of Piecewise Polynomial Trends. NeurIPS (2020)
Ming Yin, Yu Bai, Yu-Xiang Wang
Near Optimal Provable Uniform Convergence in Off-Policy Evaluation for Reinforcement Learning. CoRR (2020)
Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, Geoffrey J. Gordon
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift. NeurIPS (2020)
Hojjat Aghakhani, Dongyu Meng, Yu-Xiang Wang, Christopher Kruegel, Giovanni Vigna
Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability. CoRR (2020)
Xiaoyong Jin, Yu-Xiang Wang, Xifeng Yan
Inter-Series Attention Model for COVID-19 Forecasting. CoRR (2020)
Yuqing Zhu, Xiang Yu, Yi-Hsuan Tsai, Francesco Pittaluga, Masoud Faraki, Manmohan Chandraker, Yu-Xiang Wang
Voting-based Approaches For Differentially Private Federated Learning. CoRR (2020)
Ming Yin, Yu-Xiang Wang
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning. AISTATS (2020)
Tengyang Xie, Yifei Ma, Yu-Xiang Wang
Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling. CoRR (2019)
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani
A Higher-Order Kolmogorov-Smirnov Test. CoRR (2019)
Yifei Ma, Yu-Xiang Wang, Balakrishnan Narayanaswamy
Imitation-Regularized Offline Learning. CoRR (2019)
Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, Xifeng Yan
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting. NeurIPS (2019)
Yifei Ma, Yu-Xiang Wang, Balakrishnan Narayanaswamy
Imitation-Regularized Offline Learning. AISTATS (2019)
Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang
Provably Efficient Q-Learning with Low Switching Cost. NeurIPS (2019)
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani
A Higher-Order Kolmogorov-Smirnov Test. AISTATS (2019)
Dheeraj Baby, Yu-Xiang Wang
Online Forecasting of Total-Variation-bounded Sequences. CoRR (2019)
Xi Chen, Yining Wang, Yu-Xiang Wang
-Variation Measures. Oper. Res. 67 (6) (2019)
Dheeraj Baby, Yu-Xiang Wang
Online Forecasting of Total-Variation-bounded Sequences. NeurIPS (2019)
Yu-Xiang Wang, Borja Balle, Shiva Prasad Kasiviswanathan
Subsampled Renyi Differential Privacy and Analytical Moments Accountant. AISTATS (2019)
Yu Bai, Yu-Xiang Wang, Edo Liberty
ProxQuant: Quantized Neural Networks via Proximal Operators. ICLR (Poster) (2019)
Yu-Xiang Wang, Huan Xu, Chenlei Leng
Provable Subspace Clustering: When LRR Meets SSC. IEEE Trans. Inf. Theory 65 (9) (2019)
Tengyang Xie, Yifei Ma, Yu-Xiang Wang
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling. NeurIPS (2019)
Yu-Xiang Wang
Per-instance Differential Privacy. J. Priv. Confidentiality 9 (1) (2019)
Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang
Provably Efficient Q-Learning with Low Switching Cost. CoRR (2019)
Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, Xifeng Yan
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting. CoRR (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Yu-Xiang Wang