publications

In reversed chronological order | * denotes equal contribution | An up-to-date list is available on Google Scholar.

2023

  1. AISTATS
    A statistical analysis of Polyak-Ruppert averaged Q-learning
    Xiang Li, Wenhao Yang, Jiadong Liang, Zhihua Zhang, and Michael I Jordan
    In International Conference on Artificial Intelligence and Statistics, 2023
    arXiv Code Poster Slides Video
  2. AISTATS
    Statistical analysis of karcher means for random restricted PSD matrices
    Hengchao Chen, Xiang Li, and Qiang Sun
    In International Conference on Artificial Intelligence and Statistics, 2023
    arXiv
  3. Submitted
    Online statistical inference for nonlinear stochastic approximation with Markovian data
    Xiang Li, Jiadong Liang, and Zhihua Zhang
    arXiv preprint arXiv:2302.07690, 2023
    arXiv
  4. Submitted
    Variance-aware robust reinforcement learning with linear function approximation with heavy-tailed rewards
    Xiang Li, and Qiang Sun
    arXiv preprint arXiv:2303.05606, 2023
    arXiv
  5. Submitted
    Asymptotic behaviors and phase transitions in projected stochastic approximation: A jump diffusion approach
    Jiadong Liang, Yuze Han, Xiang Li, and Zhihua Zhang
    arXiv preprint arXiv:2304.12953, 2023
    arXiv
  6. Submitted
    Stochastic approximation MCMC, online inference, and applications in optimization of queueing systems
    Xiang Li, Jiadong Liang, Xinyun Chen, and Zhihua Zhang
    arXiv preprint arXiv:2309.09545, 2023
    arXiv

2022

  1. COLT
    Statistical estimation and online inference via Local SGD
    Xiang Li, Jiadong Liang, Xiangyu Chang, and Zhihua Zhang
    In Conference on Learning Theory, 2022
    HTML Slides
  2. NeurIPSSpotlight
    Asymptotic behaviors of projected stochastic approximation: A jump diffusion perspective
    Jiadong Liang, Yuze Han, Xiang Li, and Zhihua Zhang
    In Advances in Neural Information Processing Systems, 2022
    HTML Slides
  3. NeurIPS
    Personalized federated learning towards communication efficiency, robustness and fairness
    Shiyun Lin*, Yuze Han*, Xiang Li, and Zhihua Zhang
    In Neural Information Processing Systems, 2022
    HTML Code Poster Slides

2021

  1. ICML
    Communication-efficient distributed SVD via local power iterations
    Xiang Li, Shusen Wang, Kun Chen, and Zhihua Zhang
    In International Conference on Machine Learning, 2021
    HTML Code Poster Slides
  2. Workshop
    Finding near optimal policies via reducive regularization in Markov decision processes
    Wenhao Yang, Xiang Li, Guangzeng Xie, and Zhihua Zhang
    In Workshop on Reinforcement Learning Theory, ICML, 2021
    HTML
  3. Submitted
    Privacy-preserving distributed SVD via federated power
    Xiao Guo, Xiang Li, Xiangyu Chang, Shusen Wang, and Zhihua Zhang
    arXiv preprint arXiv:2103.00704, 2021
    arXiv

2020

  1. AAAI
    Do subsampled newton methods work for high-dimensional data?
    Xiang Li, Shusen Wang, and Zhihua Zhang
    In AAAI Conference on Artificial Intelligence, 2020
    HTML
  2. ICLROral presentaton
    On the convergence of FedAvg on non-iid data
    Xiang Li*, Kaixuan Huang*, Wenhao Yang*, Shusen Wang, and Zhihua Zhang
    In International Conference on Learning Representations, 2020
    HTML Code Poster Slides

2019

  1. NeurIPS
    A regularized approach to sparse optimal policy in reinforcement learning
    Wenhao Yang*, Xiang Li*, and Zhihua Zhang
    In Advances in Neural Information Processing Systems, 2019
    HTML
  2. Preprint
    Communication efficient decentralized training with multiple local updates
    Xiang Li, Wenhao Yang, Shusen Wang, and Zhihua Zhang
    arXiv preprint arXiv:1910.09126, 2019
    arXiv

thesis

  1. B.S. thesis
    LazySVD: Fast singular value decomposition
    Xiang Li
    Peking University, 2018, thesis
    PDF
  2. PhD. thesis
    Online statistical inference for federated learning and nonlinear stochastic approximation
    Xiang Li
    Peking University, 2023, thesis
    PDF