Yu-Xiang Wang

ORCID

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 138

Top Venues

Publications

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)
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) (2019)
Yuqing Zhu, Yu-Xiang Wang
Poission Subsampled Rényi Differential Privacy. ICML (2019)
Yu-Xiang Wang
Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain. CoRR (2018)
Borja Balle, Yu-Xiang Wang
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising. CoRR (2018)
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Anima Anandkumar
signSGD: compressed optimisation for non-convex problems. CoRR (2018)
Zachary C. Lipton, Yu-Xiang Wang, Alexander J. Smola
Detecting and Correcting for Label Shift with Black Box Predictors. CoRR (2018)

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Publication Activity (10 Years)

Top Topics

Top Venues

Yu-Xiang Wang