A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning. - Wenhao Yang; Xiang Li 050; Zhihua Zhang 004 (NeurIPS, 2019)

A Unified Framework for Regularized Reinforcement Learning. - Xiang Li 050; Wenhao Yang; Zhihua Zhang 004 (CoRR, 2019)

Communication Efficient Decentralized Training with Multiple Local Updates. - Xiang Li 050; Wenhao Yang; Shusen Wang; Zhihua Zhang 004 (CoRR, 2019)

Do Subsampled Newton Methods Work for High-Dimensional Data? - Xiang Li 050; Shusen Wang; Zhihua Zhang 004 (AAAI, 2020)

On the Convergence of FedAvg on Non-IID Data. - Xiang Li 050; Kaixuan Huang; Wenhao Yang; Shusen Wang; Zhihua Zhang 004 (ICLR, 2020)

Finding the Near Optimal Policy via Adaptive Reduced Regularization in MDPs. - Wenhao Yang; Xiang Li 050; Guangzeng Xie; Zhihua Zhang 004 (CoRR, 2020)

Communication-Efficient Distributed SVD via Local Power Iterations. - Xiang Li 050; Shusen Wang; Kun Chen; Zhihua Zhang 004 (ICML, 2021)

Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications. - Xiang Li 050; Zhihua Zhang 004 (CoRR, 2021)

Privacy-Preserving Distributed SVD via Federated Power. - Xiao Guo; Xiang Li 050; Xiangyu Chang; Shusen Wang; Zhihua Zhang 004 (CoRR, 2021)

Statistical Estimation and Inference via Local SGD in Federated Learning. - Xiang Li 050; Jiadong Liang; Xiangyu Chang; Zhihua Zhang 004 (CoRR, 2021)

Polyak-Ruppert Averaged Q-Leaning is Statistically Efficient. - Xiang Li 050; Wenhao Yang; Zhihua Zhang 004; Michael I. Jordan (CoRR, 2021)

Statistical Estimation and Online Inference via Local SGD. - Xiang Li 050; Jiadong Liang; Xiangyu Chang; Zhihua Zhang 004 (COLT, 2022)

Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective. - Jiadong Liang; Yuze Han; Xiang Li 050; Zhihua Zhang 004 (NeurIPS, 2022)

Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness. - Shiyun Lin; Yuze Han; Xiang Li 050; Zhihua Zhang 004 (NeurIPS, 2022)

A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning. - Xiang Li 050; Wenhao Yang; Jiadong Liang; Zhihua Zhang 004; Michael I. Jordan (AISTATS, 2023)

Complete Asymptotic Analysis for Projected Stochastic Approximation and Debiased Variants. - Jiadong Liang; Yuze Han; Xiang Li 050; Zhihua Zhang 004 (Allerton, 2023)

Fedpower: privacy-preserving distributed eigenspace estimation. - Xiao Guo; Xiang Li 050; Xiangyu Chang; Shusen Wang; Zhihua Zhang 004 (Mach. Learn., 2024)