default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDYu-Xiang Wang 0003
王宇翔
Person information
- unicode name: 王宇翔
- affiliation: University of California, San Diego, Halıcıoğlu Data Science Institute, CA, USA
- affiliation (former): University of California, Santa Barbara, Department of Computer Science, CA, USA
- affiliation (former): Amazon Web Services, Palo Alto, CA, USA
- affiliation (PhD): Carnegie Mellon University, Machine Learning Department, Pittsburgh, PA, USA
- affiliation (former): National University of Singapore, Department of Mechanical Engineering, Singapore
Other persons with the same name
- Yuxiang Wang (aka: Yu-xiang Wang, Yu-Xiang Wang) — disambiguation page
- Yu-Xiang Wang 0002 (aka: Yuxiang Wang 0002) — National Taiwan University, Taipei, Taiwan
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j17]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: 190-206 (2024)- [c97]Aditya Golatkar, Alessandro Achille, Luca Zancato, Yu-Xiang Wang, Ashwin Swaminathan, Stefano Soatto:
CPR: Retrieval Augmented Generation for Copyright Protection. CVPR 2024: 12374-12384 - [c96]Chuanhao Li, Chong Liu, Yu-Xiang Wang:
Communication-Efficient Federated Non-Linear Bandit Optimization. ICLR 2024 - [c95]Yingyu Lin, Yian Ma, Yu-Xiang Wang, Rachel Redberg, Zhiqi Bu:
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy. ICLR 2024 - [c94]Xuandong Zhao, Prabhanjan Vijendra Ananth, Lei Li, Yu-Xiang Wang:
Provable Robust Watermarking for AI-Generated Text. ICLR 2024 - [c93]Dan Qiao, Yu-Xiang Wang:
Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints. ICML 2024 - [c92]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Differentially Private Bias-Term Fine-tuning of Foundation Models. ICML 2024 - [c91]Songtao Feng, Ming Yin, Yu-Xiang Wang, Jing Yang, Yingbin Liang:
Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games. ICML 2024 - [c90]Antti Koskela, Rachel Redberg, Yu-Xiang Wang:
Privacy Profiles for Private Selection. ICML 2024 - [c89]Chendi Wang, Yuqing Zhu, Weijie J. Su, Yu-Xiang Wang:
Neural Collapse meets Differential Privacy: Curious behaviors of NoisyGD with Near-Perfect Representation Learning. ICML 2024 - [c88]Jianyu Xu, Yu-Xiang Wang:
Pricing with Contextual Elasticity and Heteroscedastic Valuation. ICML 2024 - [c87]Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang:
Towards General Function Approximation in Nonstationary Reinforcement Learning. ISIT 2024: 1-6 - [c86]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: 33-56 - [i103]Rachel Redberg, Antti Koskela, Yu-Xiang Wang:
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners. CoRR abs/2401.00583 (2024) - [i102]Xuandong Zhao, Xianjun Yang, Tianyu Pang, Chao Du, Lei Li, Yu-Xiang Wang, William Yang Wang:
Weak-to-Strong Jailbreaking on Large Language Models. CoRR abs/2401.17256 (2024) - [i101]Dan Qiao, Yu-Xiang Wang:
Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints. CoRR abs/2402.01111 (2024) - [i100]Ruihan Wu, Siddhartha Datta, Yi Su, Dheeraj Baby, Yu-Xiang Wang, Kilian Q. Weinberger:
Online Feature Updates Improve Online (Generalized) Label Shift Adaptation. CoRR abs/2402.03545 (2024) - [i99]Xuandong Zhao, Lei Li, Yu-Xiang Wang:
Permute-and-Flip: An optimally robust and watermarkable decoder for LLMs. CoRR abs/2402.05864 (2024) - [i98]Antti Koskela, Rachel Redberg, Yu-Xiang Wang:
Privacy Profiles for Private Selection. CoRR abs/2402.06701 (2024) - [i97]Aditya Golatkar, Alessandro Achille, Luca Zancato, Yu-Xiang Wang, Ashwin Swaminathan, Stefano Soatto:
CPR: Retrieval Augmented Generation for Copyright Protection. CoRR abs/2403.18920 (2024) - [i96]Dan Qiao, Yu-Xiang Wang:
Differentially Private Reinforcement Learning with Self-Play. CoRR abs/2404.07559 (2024) - [i95]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 abs/2405.08920 (2024) - [i94]Dan Qiao, Kaiqi Zhang, Esha Singh, Daniel Soudry, Yu-Xiang Wang:
Stable Minima Cannot Overfit in Univariate ReLU Networks: Generalization by Large Step Sizes. CoRR abs/2406.06838 (2024) - [i93]Xuandong Zhao, Chenwen Liao, Yu-Xiang Wang, Lei Li:
Efficiently Identifying Watermarked Segments in Mixed-Source Texts. CoRR abs/2410.03600 (2024) - 2023
- [j16]Peng Zhao, Yu-Hu Yan, Yu-Xiang Wang, Zhi-Hua Zhou:
Non-stationary Online Learning with Memory and Non-stochastic Control. J. Mach. Learn. Res. 24: 206:1-206:70 (2023) - [j15]Dheeraj Baby, Jianyu Xu, Yu-Xiang Wang:
Non-Stationary Contextual Pricing with Safety Constraints. Trans. Mach. Learn. Res. 2023 (2023) - [j14]Ao Liu, Yu-Xiang Wang, Lirong Xia:
Smoothed Differential Privacy. Trans. Mach. Learn. Res. 2023 (2023) - [c85]Rachel Redberg, Yuqing Zhu, Yu-Xiang Wang:
Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy. AISTATS 2023: 3977-4005 - [c84]Dheeraj Baby, Yu-Xiang Wang:
Second Order Path Variationals in Non-Stationary Online Learning. AISTATS 2023: 9024-9075 - [c83]Dan Qiao, Yu-Xiang Wang:
Near-Optimal Differentially Private Reinforcement Learning. AISTATS 2023: 9914-9940 - [c82]Jianyu Xu, Dan Qiao, Yu-Xiang Wang:
Doubly Fair Dynamic Pricing. AISTATS 2023: 9941-9975 - [c81]Dan Qiao, Yu-Xiang Wang:
Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation. ICLR 2023 - [c80]Ming Yin, Mengdi Wang, Yu-Xiang Wang:
Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient. ICLR 2023 - [c79]Kaiqi Zhang, Yu-Xiang Wang:
Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive? ICLR 2023 - [c78]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Differentially Private Optimization on Large Model at Small Cost. ICML 2023: 3192-3218 - [c77]Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang:
Non-stationary Reinforcement Learning under General Function Approximation. ICML 2023: 9976-10007 - [c76]Jiachen Li, Edwin Zhang, Ming Yin, Qinxun Bai, Yu-Xiang Wang, William Yang Wang:
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators. ICML 2023: 20485-20528 - [c75]Chong Liu, Yu-Xiang Wang:
Global Optimization with Parametric Function Approximation. ICML 2023: 22113-22136 - [c74]Xuandong Zhao, Yu-Xiang Wang, Lei Li:
Protecting Language Generation Models via Invisible Watermarking. ICML 2023: 42187-42199 - [c73]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 - [c72]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger. NeurIPS 2023 - [c71]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 - [c70]Dan Qiao, Yu-Xiang Wang:
Offline Reinforcement Learning with Differential Privacy. NeurIPS 2023 - [c69]Rachel Redberg, Antti Koskela, Yu-Xiang Wang:
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners. NeurIPS 2023 - [c68]Jiachen T. Wang, Yuqing Zhu, Yu-Xiang Wang, Ruoxi Jia, Prateek Mittal:
A Privacy-Friendly Approach to Data Valuation. NeurIPS 2023 - [c67]Chong Liu, Ming Yin, Yu-Xiang Wang:
No-Regret Linear Bandits beyond Realizability. UAI 2023: 1294-1303 - [c66]Yuqing Zhu, Xuandong Zhao, Chuan Guo, Yu-Xiang Wang:
Private Prediction Strikes Back! Private Kernelized Nearest Neighbors with Individual Rényi Filter. UAI 2023: 2586-2596 - [i92]Rachel Redberg, Yuqing Zhu, Yu-Xiang Wang:
Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy. CoRR abs/2301.00301 (2023) - [i91]Xuandong Zhao, Yu-Xiang Wang, Lei Li:
Protecting Language Generation Models via Invisible Watermarking. CoRR abs/2302.03162 (2023) - [i90]Dan Qiao, Ming Yin, Yu-Xiang Wang:
Logarithmic Switching Cost in Reinforcement Learning beyond Linear MDPs. CoRR abs/2302.12456 (2023) - [i89]Chong Liu, Ming Yin, Yu-Xiang Wang:
No-Regret Linear Bandits beyond Realizability. CoRR abs/2302.13252 (2023) - [i88]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. CoRR abs/2303.03451 (2023) - [i87]Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Matthew Jagielski, Yangsibo Huang, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie J. Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Sergey Yekhanin, Da Yu, Huanyu Zhang, Wanrong Zhang:
Challenges towards the Next Frontier in Privacy. CoRR abs/2304.06929 (2023) - [i86]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 abs/2305.19570 (2023) - [i85]Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang:
Non-stationary Reinforcement Learning under General Function Approximation. CoRR abs/2306.00861 (2023) - [i84]Xuandong Zhao, Kexun Zhang, Yu-Xiang Wang, Lei Li:
Generative Autoencoders as Watermark Attackers: Analyses of Vulnerabilities and Threats. CoRR abs/2306.01953 (2023) - [i83]Yuqing Zhu, Xuandong Zhao, Chuan Guo, Yu-Xiang Wang:
"Private Prediction Strikes Back!" Private Kernelized Nearest Neighbors with Individual Renyi Filter. CoRR abs/2306.07381 (2023) - [i82]Sunil Madhow, Dan Xiao, Ming Yin, Yu-Xiang Wang:
Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data. CoRR abs/2306.14063 (2023) - [i81]Xuandong Zhao, Prabhanjan Ananth, Lei Li, Yu-Xiang Wang:
Provable Robust Watermarking for AI-Generated Text. CoRR abs/2306.17439 (2023) - [i80]Kaiqi Zhang, Zixuan Zhang, Minshuo Chen, Mengdi Wang, Tuo Zhao, Yu-Xiang Wang:
Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks. CoRR abs/2307.01649 (2023) - [i79]Songtao Feng, Ming Yin, Yu-Xiang Wang, Jing Yang, Yingbin Liang:
Model-Free Algorithm with Improved Sample Efficiency for Zero-Sum Markov Games. CoRR abs/2308.08858 (2023) - [i78]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 abs/2308.15709 (2023) - [i77]Ruixuan Liu, Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Coupling public and private gradient provably helps optimization. CoRR abs/2310.01304 (2023) - [i76]Yingyu Lin, Yian Ma, Yu-Xiang Wang, Rachel Redberg:
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy. CoRR abs/2310.14661 (2023) - [i75]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 abs/2310.18919 (2023) - [i74]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 abs/2310.19215 (2023) - [i73]Chuanhao Li, Chong Liu, Yu-Xiang Wang:
Communication-Efficient Federated Non-Linear Bandit Optimization. CoRR abs/2311.01695 (2023) - [i72]Jianyu Xu, Yu-Xiang Wang:
Pricing with Contextual Elasticity and Heteroscedastic Valuation. CoRR abs/2312.15999 (2023) - 2022
- [j13]Chong Liu, Yu-Xiang Wang:
Doubly Robust Crowdsourcing. J. Artif. Intell. Res. 73: 209-229 (2022) - [c65]Dheeraj Baby, Yu-Xiang Wang:
Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond. AISTATS 2022: 1805-1845 - [c64]Peng Zhao, Yu-Xiang Wang, Zhi-Hua Zhou:
Non-stationary Online Learning with Memory and Non-stochastic Control. AISTATS 2022: 2101-2133 - [c63]Yuqing Zhu, Jinshuo Dong, Yu-Xiang Wang:
Optimal Accounting of Differential Privacy via Characteristic Function. AISTATS 2022: 4782-4817 - [c62]Yuqing Zhu, Yu-Xiang Wang:
Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE. AISTATS 2022: 5622-5635 - [c61]Jianyu Xu, Yu-Xiang Wang:
Towards Agnostic Feature-based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise. AISTATS 2022: 9643-9662 - [c60]Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto:
Mixed Differential Privacy in Computer Vision. CVPR 2022: 8366-8376 - [c59]Xuandong Zhao, Lei Li, Yu-Xiang Wang:
Distillation-Resistant Watermarking for Model Protection in NLP. EMNLP (Findings) 2022: 5044-5055 - [c58]Ming Yin, Yaqi Duan, Mengdi Wang, Yu-Xiang Wang:
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism. ICLR 2022 - [c57]Dan Qiao, Ming Yin, Ming Min, Yu-Xiang Wang:
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost. ICML 2022: 18031-18061 - [c56]Xuandong Zhao, Lei Li, Yu-Xiang Wang:
Provably Confidential Language Modelling. NAACL-HLT 2022: 943-955 - [c55]Dheeraj Baby, Yu-Xiang Wang:
Optimal Dynamic Regret in LQR Control. NeurIPS 2022 - [c54]Zhiliang Tian, Yingxiu Zhao, Ziyue Huang, Yu-Xiang Wang, Nevin L. Zhang, He He:
SeqPATE: Differentially Private Text Generation via Knowledge Distillation. NeurIPS 2022 - [c53]Fuheng Zhao, Dan Qiao, Rachel Redberg, Divyakant Agrawal, Amr El Abbadi, Yu-Xiang Wang:
Differentially Private Linear Sketches: Efficient Implementations and Applications. NeurIPS 2022 - [c52]Ming Yin, Wenjing Chen, Mengdi Wang, Yu-Xiang Wang:
Offline stochastic shortest path: Learning, evaluation and towards optimality. UAI 2022: 2278-2288 - [i71]Dheeraj Baby, Yu-Xiang Wang:
Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond. CoRR abs/2201.08905 (2022) - [i70]Jianyu Xu, Yu-Xiang Wang:
Towards Agnostic Feature-based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise. CoRR abs/2201.11341 (2022) - [i69]Dan Qiao, Ming Yin, Ming Min, Yu-Xiang Wang:
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost. CoRR abs/2202.06385 (2022) - [i68]Ming Yin, Yaqi Duan, Mengdi Wang, Yu-Xiang Wang:
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism. CoRR abs/2203.05804 (2022) - [i67]Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto:
Mixed Differential Privacy in Computer Vision. CoRR abs/2203.11481 (2022) - [i66]Yuqing Zhu, Yu-Xiang Wang:
Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE. CoRR abs/2203.16100 (2022) - [i65]Simone Bombari, Alessandro Achille, Zijian Wang, Yu-Xiang Wang, Yusheng Xie, Kunwar Yashraj Singh, Srikar Appalaraju, Vijay Mahadevan, Stefano Soatto:
Towards Differential Relational Privacy and its use in Question Answering. CoRR abs/2203.16701 (2022) - [i64]Kaiqi Zhang, Yu-Xiang Wang:
Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive? CoRR abs/2204.09664 (2022) - [i63]Xuandong Zhao, Lei Li, Yu-Xiang Wang:
Provably Confidential Language Modelling. CoRR abs/2205.01863 (2022) - [i62]Dheeraj Baby, Yu-Xiang Wang:
Second Order Path Variationals in Non-Stationary Online Learning. CoRR abs/2205.01921 (2022) - [i61]Fuheng Zhao, Dan Qiao, Rachel Redberg, Divyakant Agrawal, Amr El Abbadi, Yu-Xiang Wang:
Differentially Private Linear Sketches: Efficient Implementations and Applications. CoRR abs/2205.09873 (2022) - [i60]Dan Qiao, Yu-Xiang Wang:
Offline Reinforcement Learning with Differential Privacy. CoRR abs/2206.00810 (2022) - [i59]Ming Yin, Wenjing Chen, Mengdi Wang, Yu-Xiang Wang:
Offline Stochastic Shortest Path: Learning, Evaluation and Towards Optimality. CoRR abs/2206.04921 (2022) - [i58]Kaiqi Zhang, Ming Yin, Yu-Xiang Wang:
Why Quantization Improves Generalization: NTK of Binary Weight Neural Networks. CoRR abs/2206.05916 (2022) - [i57]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger. CoRR abs/2206.07136 (2022) - [i56]Dheeraj Baby, Yu-Xiang Wang:
Optimal Dynamic Regret in LQR Control. CoRR abs/2206.09257 (2022) - [i55]Jianyu Xu, Dan Qiao, Yu-Xiang Wang:
Doubly Fair Dynamic Pricing. CoRR abs/2209.11837 (2022) - [i54]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Differentially Private Bias-Term only Fine-tuning of Foundation Models. CoRR abs/2210.00036 (2022) - [i53]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Differentially Private Optimization on Large Model at Small Cost. CoRR abs/2210.00038 (2022) - [i52]Dan Qiao, Yu-Xiang Wang:
Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation. CoRR abs/2210.00701 (2022) - [i51]Ming Yin, Mengdi Wang, Yu-Xiang Wang:
Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient. CoRR abs/2210.00750 (2022) - [i50]Xuandong Zhao, Lei Li, Yu-Xiang Wang:
Distillation-Resistant Watermarking for Model Protection in NLP. CoRR abs/2210.03312 (2022) - [i49]Chong Liu, Yu-Xiang Wang:
Global Optimization with Parametric Function Approximation. CoRR abs/2211.09100 (2022) - [i48]Jiachen Li, Edwin Zhang, Ming Yin, Qinxun Bai, Yu-Xiang Wang, William Yang Wang:
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators. CoRR abs/2211.15956 (2022) - [i47]Dan Qiao, Yu-Xiang Wang:
Near-Optimal Differentially Private Reinforcement Learning. CoRR abs/2212.04680 (2022) - 2021
- [j12]Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang:
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. J. Mach. Learn. Res. 22: 262:1-262:44 (2021) - [c51]Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang:
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. AISTATS 2021: 838-846 - [c50]Ming Yin, Yu Bai, Yu-Xiang Wang:
Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning. AISTATS 2021: 1567-1575 - [c49]Dheeraj Baby, Xuandong Zhao, Yu-Xiang Wang:
An Optimal Reduction of TV-Denoising to Adaptive Online Learning. AISTATS 2021: 2899-2907 - [c48]Dheeraj Baby, Yu-Xiang Wang:
Optimal Dynamic Regret in Exp-Concave Online Learning. COLT 2021: 359-409 - [c47]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: 159-178 - [c46]Ming Yin, Yu-Xiang Wang:
Towards Instance-Optimal Offline Reinforcement Learning with Pessimism. NeurIPS 2021: 4065-4078 - [c45]Ming Yin, Yu Bai, Yu-Xiang Wang:
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction. NeurIPS 2021: 7677-7688 - [c44]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: 12890-12903 - [c43]Jianyu Xu, Yu-Xiang Wang:
Logarithmic Regret in Feature-based Dynamic Pricing. NeurIPS 2021: 13898-13910 - [c42]Rachel Redberg, Yu-Xiang Wang:
Privately Publishable Per-instance Privacy. NeurIPS 2021: 17335-17346 - [c41]Xiaoyong Jin, Yu-Xiang Wang, Xifeng Yan:
Inter-Series Attention Model for COVID-19 Forecasting. SDM 2021: 495-503 - [i46]Dheeraj Baby, Xuandong Zhao, Yu-Xiang Wang:
An Optimal Reduction of TV-Denoising to Adaptive Online Learning. CoRR abs/2101.09438 (2021) - [i45]Ming Yin, Yu Bai, Yu-Xiang Wang:
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction. CoRR abs/2102.01748 (2021) - [i44]Peng Zhao, Yu-Xiang Wang, Zhi-Hua Zhou:
Non-stationary Online Learning with Memory and Non-stochastic Control. CoRR abs/2102.03758 (2021) - [i43]Jianyu Xu, Yu-Xiang Wang:
Logarithmic Regret in Feature-based Dynamic Pricing. CoRR abs/2102.10221 (2021) - [i42]Dheeraj Baby, Yu-Xiang Wang:
Optimal Dynamic Regret in Exp-Concave Online Learning. CoRR abs/2104.11824 (2021) - [i41]Ming Yin, Yu-Xiang Wang:
Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings. CoRR abs/2105.06029 (2021) - [i40]Yuqing Zhu, Jinshuo Dong, Yu-Xiang Wang:
Optimal Accounting of Differential Privacy via Characteristic Function. CoRR abs/2106.08567 (2021) - [i39]Ming Yin, Yu-Xiang Wang:
Towards Instance-Optimal Offline Reinforcement Learning with Pessimism. CoRR abs/2110.08695 (2021) - [i38]Rachel Redberg, Yu-Xiang Wang:
Privately Publishable Per-instance Privacy. CoRR abs/2111.02281 (2021) - [i37]Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Addison J. Hu, Ryan J. Tibshirani:
Multivariate Trend Filtering for Lattice Data. CoRR abs/2112.14758 (2021) - 2020
- [j11]Yu-Xiang Wang, Borja Balle, Shiva Prasad Kasiviswanathan:
Subsampled Rényi Differential Privacy and Analytical Moments Accountant. J. Priv. Confidentiality 10(2) (2020) - [j10]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): 1139-1152 (2020) - [c40]Ming Yin, Yu-Xiang Wang:
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning. AISTATS 2020: 3948-3958 - [c39]Yuqing Zhu, Xiang Yu, Manmohan Chandraker, Yu-Xiang Wang:
Private-kNN: Practical Differential Privacy for Computer Vision. CVPR 2020: 11851-11859 - [c38]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: 2421-2431 - [c37]Yuqing Zhu, Yu-Xiang Wang:
Improving Sparse Vector Technique with Renyi Differential Privacy. NeurIPS 2020 - [c36]Dheeraj Baby, Yu-Xiang Wang:
Adaptive Online Estimation of Piecewise Polynomial Trends. NeurIPS 2020 - [c35]Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, Geoffrey J. Gordon:
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift. NeurIPS 2020 - [i36]Ming Yin, Yu-Xiang Wang:
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning. CoRR abs/2001.10742 (2020) - [i35]Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, Geoffrey J. Gordon:
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift. CoRR abs/2003.04475 (2020) - [i34]Hojjat Aghakhani, Dongyu Meng, Yu-Xiang Wang, Christopher Kruegel, Giovanni Vigna:
Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability. CoRR abs/2005.00191 (2020) - [i33]Ming Yin, Yu Bai, Yu-Xiang Wang:
Near Optimal Provable Uniform Convergence in Off-Policy Evaluation for Reinforcement Learning. CoRR abs/2007.03760 (2020) - [i32]Dheeraj Baby, Yu-Xiang Wang:
Adaptive Online Estimation of Piecewise Polynomial Trends. CoRR abs/2010.00073 (2020) - [i31]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 abs/2010.04851 (2020) - [i30]Xiaoyong Jin, Yu-Xiang Wang, Xifeng Yan:
Inter-Series Attention Model for COVID-19 Forecasting. CoRR abs/2010.13006 (2020) - [i29]Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang:
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. CoRR abs/2011.03186 (2020)
2010 – 2019
- 2019
- [j9]Xi Chen, Yining Wang, Yu-Xiang Wang:
Technical Note - Nonstationary Stochastic Optimization Under Lp, q-Variation Measures. Oper. Res. 67(6): 1752-1765 (2019) - [j8]Yu-Xiang Wang:
Per-instance Differential Privacy. J. Priv. Confidentiality 9(1) (2019) - [j7]Yining Wang, Yu-Xiang Wang, Aarti Singh:
A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data. IEEE Trans. Inf. Theory 65(2): 685-706 (2019) - [j6]Yu-Xiang Wang, Huan Xu, Chenlei Leng:
Provable Subspace Clustering: When LRR Meets SSC. IEEE Trans. Inf. Theory 65(9): 5406-5432 (2019) - [c34]Yu-Xiang Wang, Borja Balle, Shiva Prasad Kasiviswanathan:
Subsampled Renyi Differential Privacy and Analytical Moments Accountant. AISTATS 2019: 1226-1235 - [c33]Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani:
A Higher-Order Kolmogorov-Smirnov Test. AISTATS 2019: 2621-2630 - [c32]Yifei Ma, Yu-Xiang Wang, Balakrishnan Narayanaswamy:
Imitation-Regularized Offline Learning. AISTATS 2019: 2956-2965 - [c31]Yu Bai, Yu-Xiang Wang, Edo Liberty:
ProxQuant: Quantized Neural Networks via Proximal Operators. ICLR (Poster) 2019 - [c30]Yuqing Zhu, Yu-Xiang Wang:
Poission Subsampled Rényi Differential Privacy. ICML 2019: 7634-7642 - [c29]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: 5244-5254 - [c28]Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang:
Provably Efficient Q-Learning with Low Switching Cost. NeurIPS 2019: 8002-8011 - [c27]Tengyang Xie, Yifei Ma, Yu-Xiang Wang:
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling. NeurIPS 2019: 9665-9675 - [c26]Dheeraj Baby, Yu-Xiang Wang:
Online Forecasting of Total-Variation-bounded Sequences. NeurIPS 2019: 11069-11079 - [i28]Yifei Ma, Yu-Xiang Wang, Balakrishnan Narayanaswamy:
Imitation-Regularized Offline Learning. CoRR abs/1901.04723 (2019) - [i27]Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani:
A Higher-Order Kolmogorov-Smirnov Test. CoRR abs/1903.10083 (2019) - [i26]Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang:
Provably Efficient Q-Learning with Low Switching Cost. CoRR abs/1905.12849 (2019) - [i25]Dheeraj Baby, Yu-Xiang Wang:
Online Forecasting of Total-Variation-bounded Sequences. CoRR abs/1906.03364 (2019) - [i24]Tengyang Xie, Yifei Ma, Yu-Xiang Wang:
Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling. CoRR abs/1906.03393 (2019) - [i23]Chong Liu, Yu-Xiang Wang:
Doubly Robust Crowdsourcing. CoRR abs/1906.08591 (2019) - [i22]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 abs/1907.00235 (2019) - 2018
- [c25]Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Anima Anandkumar:
Compression by the signs: distributed learning is a two-way street. ICLR (Workshop) 2018 - [c24]Borja Balle, Yu-Xiang Wang:
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising. ICML 2018: 403-412 - [c23]Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Animashree Anandkumar:
SIGNSGD: Compressed Optimisation for Non-Convex Problems. ICML 2018: 559-568 - [c22]Zachary C. Lipton, Yu-Xiang Wang, Alexander J. Smola:
Detecting and Correcting for Label Shift with Black Box Predictors. ICML 2018: 3128-3136 - [c21]Yu-Xiang Wang:
Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain. UAI 2018: 93-103 - [i21]Zachary C. Lipton, Yu-Xiang Wang, Alexander J. Smola:
Detecting and Correcting for Label Shift with Black Box Predictors. CoRR abs/1802.03916 (2018) - [i20]Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Anima Anandkumar:
signSGD: compressed optimisation for non-convex problems. CoRR abs/1802.04434 (2018) - [i19]Yu-Xiang Wang:
Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain. CoRR abs/1803.02596 (2018) - [i18]Borja Balle, Yu-Xiang Wang:
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising. CoRR abs/1805.06530 (2018) - [i17]Chieh-Chi Kao, Yu-Xiang Wang, Jonathan Waltman, Pradeep Sen:
Patch-Based Image Hallucination for Super Resolution with Detail Reconstruction from Similar Sample Images. CoRR abs/1806.00874 (2018) - [i16]Yu-Xiang Wang, Borja Balle, Shiva Prasad Kasiviswanathan:
Subsampled Rényi Differential Privacy and Analytical Moments Accountant. CoRR abs/1808.00087 (2018) - [i15]Yu Bai, Yu-Xiang Wang, Edo Liberty:
ProxQuant: Quantized Neural Networks via Proximal Operators. CoRR abs/1810.00861 (2018) - 2017
- [b1]Yu-Xiang Wang:
New Paradigms and Optimality Guarantees in Statistical Learning and Estimation. Carnegie Mellon University, USA, 2017 - [c20]Ziqi Liu, Alexander J. Smola, Kyle Soska, Yu-Xiang Wang, Qinghua Zheng:
Attributing Hacks. AISTATS 2017: 794-802 - [c19]Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudík:
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits. ICML 2017: 3589-3597 - [c18]Veeranjaneyulu Sadhanala, Yu-Xiang Wang, James Sharpnack, Ryan J. Tibshirani:
Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods. NIPS 2017: 5800-5810 - [i14]Yu-Xiang Wang:
Per-instance Differential Privacy and the Adaptivity of Posterior Sampling in Linear and Ridge regression. CoRR abs/1707.07708 (2017) - [i13]Xi Chen, Yining Wang, Yu-Xiang Wang:
Non-stationary Stochastic Optimization with Local Spatial and Temporal Changes. CoRR abs/1708.03020 (2017) - 2016
- [j5]Yu-Xiang Wang, Huan Xu:
Noisy Sparse Subspace Clustering. J. Mach. Learn. Res. 17: 12:1-12:41 (2016) - [j4]Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani:
Trend Filtering on Graphs. J. Mach. Learn. Res. 17: 105:1-105:41 (2016) - [j3]Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg:
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle. J. Mach. Learn. Res. 17: 183:1-183:40 (2016) - [c17]Yining Wang, Yu-Xiang Wang, Aarti Singh:
Graph Connectivity in Noisy Sparse Subspace Clustering. AISTATS 2016: 538-546 - [c16]Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J. Tibshirani:
Graph Sparsification Approaches for Laplacian Smoothing. AISTATS 2016: 1250-1259 - [c15]Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric P. Xing:
Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms. ICML 2016: 1548-1557 - [c14]Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J. Tibshirani:
Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers. NIPS 2016: 3513-3521 - [c13]Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg:
On-Average KL-Privacy and Its Equivalence to Generalization for Max-Entropy Mechanisms. PSD 2016: 121-134 - [c12]Mu Li, Ziqi Liu, Alexander J. Smola, Yu-Xiang Wang:
DiFacto: Distributed Factorization Machines. WSDM 2016: 377-386 - [i12]Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg:
A Minimax Theory for Adaptive Data Analysis. CoRR abs/1602.04287 (2016) - [i11]Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg:
On-Average KL-Privacy and its equivalence to Generalization for Max-Entropy Mechanisms. CoRR abs/1605.02277 (2016) - [i10]Yining Wang, Yu-Xiang Wang, Aarti Singh:
A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data. CoRR abs/1610.07650 (2016) - [i9]Ziqi Liu, Alexander J. Smola, Kyle Soska, Yu-Xiang Wang, Qinghua Zheng:
Attributing Hacks. CoRR abs/1611.03021 (2016) - [i8]Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudík:
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits. CoRR abs/1612.01205 (2016) - 2015
- [j2]Yu-Xiang Wang, Choon Meng Lee, Loong-Fah Cheong, Kim-Chuan Toh:
Practical Matrix Completion and Corruption Recovery Using Proximal Alternating Robust Subspace Minimization. Int. J. Comput. Vis. 111(3): 315-344 (2015) - [c11]Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani:
Trend Filtering on Graphs. AISTATS 2015 - [c10]Yining Wang, Yu-Xiang Wang, Aarti Singh:
A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data. ICML 2015: 1422-1431 - [c9]Yu-Xiang Wang, Stephen E. Fienberg, Alexander J. Smola:
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo. ICML 2015: 2493-2502 - [c8]Seth R. Flaxman, Yu-Xiang Wang, Alexander J. Smola:
Who Supported Obama in 2012?: Ecological Inference through Distribution Regression. KDD 2015: 289-298 - [c7]Yining Wang, Yu-Xiang Wang, Aarti Singh:
Differentially private subspace clustering. NIPS 2015: 1000-1008 - [c6]Ziqi Liu, Yu-Xiang Wang, Alexander J. Smola:
Fast Differentially Private Matrix Factorization. RecSys 2015: 171-178 - [i7]Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg:
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle. CoRR abs/1502.06309 (2015) - [i6]Yu-Xiang Wang, Stephen E. Fienberg, Alexander J. Smola:
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo. CoRR abs/1502.07645 (2015) - [i5]Yining Wang, Yu-Xiang Wang, Aarti Singh:
Clustering Consistent Sparse Subspace Clustering. CoRR abs/1504.01046 (2015) - [i4]Ziqi Liu, Yu-Xiang Wang, Alexander J. Smola:
Fast Differentially Private Matrix Factorization. CoRR abs/1505.01419 (2015) - 2014
- [j1]Zhi Gao, Loong-Fah Cheong, Yu-Xiang Wang:
Block-Sparse RPCA for Salient Motion Detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(10): 1975-1987 (2014) - [c5]Yu-Xiang Wang, Alexander J. Smola, Ryan J. Tibshirani:
The Falling Factorial Basis and Its Statistical Applications. ICML 2014: 730-738 - [i3]Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani:
Trend Filtering on Graphs. CoRR abs/1410.7690 (2014) - 2013
- [c4]Yu-Xiang Wang, Huan Xu:
Noisy Sparse Subspace Clustering. ICML (1) 2013: 89-97 - [c3]Yu-Xiang Wang, Huan Xu, Chenlei Leng:
Provable Subspace Clustering: When LRR meets SSC. NIPS 2013: 64-72 - [i2]Yu-Xiang Wang, Choon Meng Lee, Loong-Fah Cheong, Kim-Chuan Toh:
Practical Matrix Completion and Corruption Recovery using Proximal Alternating Robust Subspace Minimization. CoRR abs/1309.1539 (2013) - 2012
- [c2]Yu-Xiang Wang, Huan Xu:
Stability of matrix factorization for collaborative filtering. ICML 2012 - [i1]Yu-Xiang Wang, Huan Xu:
Stability of matrix factorization for collaborative filtering. CoRR abs/1206.4640 (2012) - 2011
- [c1]Yu-Xiang Wang, Bin Wang, Li Chen:
Real-Time Volume Caustics with Image-Based Photon Tracing. CAD/Graphics 2011: 71-78
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-18 20:45 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
