Publications

* denotes equal contribution or alphabetic ordering
# denotes supervised student paper

2025

• A Sequential Stopping Procedure for Statistical Estimation with Infinite Variance
  Jose Blanchet*, Peter Glynn*, Wenhao Yang*
  To be submitted

• Statistical Inference for the Stochastic Gradient Descent with Infinite Variance
  Jose Blanchet*, Peter Glynn*, Wenhao Yang*
  To be submitted

• Wasserstein Distributionally Robust Policy Learning with Continuous Context
  Jose Blanchet*, Miao Lu*, Wenhao Yang*, Zhengyuan Zhou*
  To be submitted

2024

• Limit Theorems for Stochastic Gradient Descent with Infinite Variance
  Jose Blanchet*, Aleksandar Mijatović*, Wenhao Yang*
  The Annals of Applied Probability, Major Revision.

• Distributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions
  Patrick Kuiper, Ali Hasan, Wenhao Yang, Yuting Ng, Hoda Bidkhori, Jose Blanchet, Vahid Tarokh
  The 40th Conference on Uncertainty in Artificial Intelligence (UAI) 2024

2023

• Estimation and Inference in Distributional Reinforcement Learning
  Liangyu Zhang, Yang Peng, Jiadong Liang, Wenhao Yang#, Zhihua Zhang
  The Annals of Statistics, Accepted.

• Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning
  Liangyu Zhang, Yang Peng, Wenhao Yang, Zhihua Zhang
  Transactions on Pattern Analysis and Machine Intelligence

• Semiparametrically Efficient Off-Policy Evaluation in Linear Markov Decision Processes
  Chuhan Xie, Wenhao Yang#, Zhihua Zhang
  International Conference on Machine Learning (ICML) 2023

• Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
  Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre MENARD, Mohammad Gheshlaghi Azar, Remi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvari, Wataru Kumagai, Yutaka Matsuo
  International Conference on Machine Learning (ICML) 2023

• Robust Markov Decision Processes without Model Estimation
  Wenhao Yang, Han Wang, Tadashi Kozuno, Scott M. Jordan, Zhihua Zhang

2022

• Semi-Infinitely Constrained Markov Decision Processes
  Liangyu Zhang, Yang Peng, Wenhao Yang, Zhihua Zhang
  Conference on Neural Information Processing Systems (NeurIPS) 2022.

• Statistical Estimation of Confounded Linear MDPs: An Instrumental Variable Approach
  Miao Lu*, Wenhao Yang*, Liangyu Zhang*, Zhihua Zhang*

• KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal
  Tadashi Kozuno, Wenhao Yang, Nino Vieillard, Toshinori Kitamura, Yunhao Tang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Michal Valko, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári

• Federated Reinforcement Learning with Environment Heterogeneity
  Hao Jin, Yang Peng, Wenhao Yang, Shusen Wang, Zhihua Zhang
  International Conference on Artificial Intelligence and Statistics (AISTATS 2022).

2021

• Polyak-Ruppert-Averaged Q-Learning is Statistically Efficient
  Xiang Li, Wenhao Yang, Jiadong Liang, Zhihua Zhang, Michael I. Jordan
  International Conference on Artificial Intelligence and Statistics (AISTATS 2023)

• Towards Theoretical Understandings of Robust Markov Decision Processes: Sample Complexity and Asymptotics
  Wenhao Yang, Liangyu Zhang, Zhihua Zhang
  The Annals of Statistics 2022, Vol. 50, No. 6, 3223-3248.

2020

• Finding the Near Optimal Policy via Adaptive Reduced Regularization in MDPs
  Wenhao Yang, Xiang Li, Guangzeng Xie, Zhihua Zhang
  Workshop on Reinforcement Learning Theory at ICML 2021.

• On the Convergence of FedAvg on Non-IID Data
  Xiang Li*, Kaixuan Huang*, Wenhao Yang*, Shusen Wang, Zhihua Zhang
  International Conference on Learning Representations (ICLR) 2020. (Oral)

2019

• Communication Efficient Decentralized Training with Multiple Local Updates
  Xiang Li, Wenhao Yang, Shusen Wang, Zhihua Zhang

• A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning
  Xiang Li*, Wenhao Yang*, Zhihua Zhang
  Conference on Neural Information Processing Systems (NeurIPS) 2019.