Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy. - Han Zhong 001; Ethan X. Fang; Zhuoran Yang; Zhaoran Wang 001 (CoRR, 2020)

Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs. - Han Zhong 001; Jiayi Huang; Lin Yang 011; Liwei Wang 001 (NeurIPS, 2021)

A Unified Framework for Conservative Exploration. - Yunchang Yang; Tianhao Wu 002; Han Zhong 001; Evrard Garcelon; Matteo Pirotta; Alessandro Lazaric; Liwei Wang 001; Simon S. Du (CoRR, 2021)

Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs. - Han Zhong 001; Zhuoran Yang; Zhaoran Wang 001; Csaba Szepesvári (CoRR, 2021)

Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopic Followers? - Han Zhong 001; Zhuoran Yang; Zhaoran Wang 001; Michael I. Jordan (CoRR, 2021)

A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning. - Yunchang Yang; Tianhao Wu 002; Han Zhong 001; Evrard Garcelon; Matteo Pirotta; Alessandro Lazaric; Liwei Wang 001; Simon Shaolei Du (ICLR, 2022)

Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation. - Xiaoyu Chen 008; Han Zhong 001; Zhuoran Yang; Zhaoran Wang 001; Liwei Wang 001 (ICML, 2022)

Nearly Optimal Policy Optimization with Stable at Any Time Guarantee. - Tianhao Wu 002; Yunchang Yang; Han Zhong 001; Liwei Wang 001; Simon S. Du; Jiantao Jiao (ICML, 2022)

A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games. - Wei Xiong 015; Han Zhong 001; Chengshuai Shi; Cong Shen 001; Tong Zhang 001 (ICML, 2022)

Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets. - Han Zhong 001; Wei Xiong 015; Jiyuan Tan; Liwei Wang 001; Tong Zhang 001; Zhaoran Wang 001; Zhuoran Yang (ICML, 2022)

Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power. - Binghui Li; Jikai Jin; Han Zhong 001; John E. Hopcroft; Liwei Wang 001 (NeurIPS, 2022)

GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond. - Han Zhong 001; Wei Xiong 015; Sirui Zheng; Liwei Wang 001; Zhaoran Wang 001; Zhuoran Yang; Tong Zhang 001 (CoRR, 2022)

Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game. - Wei Xiong 015; Han Zhong 001; Chengshuai Shi; Cong Shen 001; Liwei Wang 001; Tong Zhang 001 (ICLR, 2023)

Provable Sim-to-real Transfer in Continuous Domain with Partial Observations. - Jiachen Hu; Han Zhong 001; Chi Jin 001; Liwei Wang 001 (ICLR, 2023)

A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes. - Han Zhong 001; Tong Zhang 001 (NeurIPS, 2023)

Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage. - Jose H. Blanchet; Miao Lu; Tong Zhang 001; Han Zhong 001 (NeurIPS, 2023)

Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds. - Jiayi Huang; Han Zhong 001; Liwei Wang 001; Lin Yang 011 (NeurIPS, 2023)

Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration. - Zhihan Liu; Miao Lu; Wei Xiong 015; Han Zhong 001; Hao Hu; Shenao Zhang; Sirui Zheng; Zhuoran Yang; Zhaoran Wang 001 (NeurIPS, 2023)

Posterior Sampling for Competitive RL: Function Approximation and Partial Observation. - Shuang Qiu; Ziyu Dai; Han Zhong 001; Zhaoran Wang 001; Zhuoran Yang; Tong Zhang 001 (NeurIPS, 2023)

A Reduction-based Framework for Sequential Decision Making with Delayed Feedback. - Yunchang Yang; Han Zhong 001; Tianhao Wu 002; Bin Liu; Liwei Wang 001; Simon S. Du (NeurIPS, 2023)

One Objective to Rule Them All: A Maximization Objective Fusing Estimation and Planning for Exploration. - Zhihan Liu; Miao Lu; Wei Xiong 015; Han Zhong 001; Hao Hu; Shenao Zhang; Sirui Zheng; Zhuoran Yang; Zhaoran Wang 001 (CoRR, 2023)

Gibbs Sampling from Human Feedback: A Provable KL- constrained Framework for RLHF. - Wei Xiong 015; Hanze Dong; Chenlu Ye; Han Zhong 001; Nan Jiang 008; Tong Zhang 001 (CoRR, 2023)

Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers? - Han Zhong 001; Zhuoran Yang; Zhaoran Wang 001; Michael I. Jordan (J. Mach. Learn. Res., 2023)

Horizon-Free and Instance-Dependent Regret Bounds for Reinforcement Learning with General Function Approximation. - Jiayi Huang; Han Zhong 001; Liwei Wang 001; Lin Yang 011 (AISTATS, 2024)

Towards Robust Offline Reinforcement Learning under Diverse Data Corruption. - Rui Yang 010; Han Zhong 001; Jiawei Xu; Amy Zhang 001; Chongjie Zhang; Lei Han 001; Tong Zhang 001 (ICLR, 2024)

Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret. - Han Zhong 001; Jiachen Hu; Yecheng Xue; Tongyang Li; Liwei Wang 001 (ICML, 2024)

Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment. - Rui Yang 010; Xiaoman Pan; Feng Luo; Shuang Qiu; Han Zhong 001; Dong Yu 001; Jianshu Chen (ICML, 2024)

Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint. - Wei Xiong 015; Hanze Dong; Chenlu Ye; Ziqi Wang 003; Han Zhong 001; Heng Ji 001; Nan Jiang 008; Tong Zhang 001 (ICML, 2024)

Rethinking Model-based, Policy-based, and Value-based Reinforcement Learning via the Lens of Representation Complexity. - Guhao Feng; Han Zhong 001 (NeurIPS, 2024)

Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms. - Miao Lu; Han Zhong 001; Tong Zhang 001; Jose H. Blanchet (NeurIPS, 2024)

Quantum Non-Identical Mean Estimation: Efficient Algorithms and Fundamental Limits. - Jiachen Hu; Tongyang Li; Xinzhao Wang; Yecheng Xue; Chenyi Zhang 003; Han Zhong 001 (TQC, 2024)

Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm. - Miao Lu; Han Zhong 001; Tong Zhang 001; Jose H. Blanchet (CoRR, 2024)

DPO Meets PPO: Reinforced Token Optimization for RLHF. - Han Zhong 001; Guhao Feng; Wei Xiong 015; Li Zhao 007; Di He 001; Jiang Bian 002; Liwei Wang 001 (CoRR, 2024)

Self-Exploring Language Models: Active Preference Elicitation for Online Alignment. - Shenao Zhang; Donghan Yu; Hiteshi Sharma; Han Zhong 001; Zhihan Liu; Ziyi Yang; Shuohang Wang; Hany Hassan Awadalla; Zhaoran Wang 001 (Trans. Mach. Learn. Res., 2025)

BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning. - Han Zhong 001; Yutong Yin; Shenao Zhang; Xiaojun Xu; Yuanxin Liu; Yifei Zuo; Zhihan Liu; Boyi Liu 001; Sirui Zheng; Hongyi Guo; Liwei Wang 001; Mingyi Hong 001; et al. (CoRR, 2025)

Learning an Optimal Assortment Policy under Observational Data. - Yuxuan Han; Han Zhong 001; Miao Lu; Jose H. Blanchet; Zhengyuan Zhou (CoRR, 2025)

The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability. - Jiachen Hu; Rui Ai 002; Han Zhong 001; Xiaoyu Chen 008; Liwei Wang 001; Zhaoran Wang 001; Zhuoran Yang (CoRR, 2025)