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ORCIDHan Zhong 0001
Person information
- unicode name: 钟涵
- affiliation: Peking University, Center for Data Science, Beijing, China
Other persons with the same name
- Han Zhong — disambiguation page
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2020 – today
- 2025
[j2]Shenao Zhang, Donghan Yu, Hiteshi Sharma, Han Zhong, Zhihan Liu, Ziyi Yang, Shuohang Wang, Hany Hassan Awadalla, Zhaoran Wang:
Self-Exploring Language Models: Active Preference Elicitation for Online Alignment. Trans. Mach. Learn. Res. 2025 (2025)- [i29]Han Zhong, Yutong Yin, Shenao Zhang, Xiaojun Xu, Yuanxin Liu, Yifei Zuo, Zhihan Liu, Boyi Liu, Sirui Zheng, Hongyi Guo, Liwei Wang, Mingyi Hong, Zhaoran Wang:
BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning. CoRR abs/2501.18858 (2025) - [i28]Yuxuan Han, Han Zhong, Miao Lu, Jose H. Blanchet, Zhengyuan Zhou:
Learning an Optimal Assortment Policy under Observational Data. CoRR abs/2502.06777 (2025) - [i27]Jiachen Hu, Rui Ai, Han Zhong, Xiaoyu Chen, Liwei Wang, Zhaoran Wang, Zhuoran Yang:
The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability. CoRR abs/2506.09940 (2025) - 2024
- [c23]Jiayi Huang, Han Zhong, Liwei Wang, Lin Yang:
Horizon-Free and Instance-Dependent Regret Bounds for Reinforcement Learning with General Function Approximation. AISTATS 2024: 3673-3681 - [c22]Rui Yang, Han Zhong, Jiawei Xu, Amy Zhang, Chongjie Zhang, Lei Han, Tong Zhang:
Towards Robust Offline Reinforcement Learning under Diverse Data Corruption. ICLR 2024 - [c21]Han Zhong, Jiachen Hu, Yecheng Xue, Tongyang Li, Liwei Wang:
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret. ICML 2024 - [c20]Rui Yang, Xiaoman Pan, Feng Luo, Shuang Qiu, Han Zhong, Dong Yu, Jianshu Chen:
Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment. ICML 2024 - [c19]Wei Xiong, Hanze Dong, Chenlu Ye, Ziqi Wang, Han Zhong, Heng Ji, Nan Jiang, Tong Zhang:
Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint. ICML 2024 - [c18]Guhao Feng, Han Zhong:
Rethinking Model-based, Policy-based, and Value-based Reinforcement Learning via the Lens of Representation Complexity. NeurIPS 2024 - [c17]Miao Lu, Han Zhong, Tong Zhang, Jose H. Blanchet:
Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms. NeurIPS 2024 - [c16]Jiachen Hu, Tongyang Li, Xinzhao Wang, Yecheng Xue, Chenyi Zhang, Han Zhong:
Quantum Non-Identical Mean Estimation: Efficient Algorithms and Fundamental Limits. TQC 2024: 9:1-9:21 - [i26]Rui Yang, Xiaoman Pan, Feng Luo, Shuang Qiu, Han Zhong, Dong Yu, Jianshu Chen:
Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment. CoRR abs/2402.10207 (2024) - [i25]Miao Lu, Han Zhong, Tong Zhang, Jose H. Blanchet:
Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm. CoRR abs/2404.03578 (2024) - [i24]Han Zhong, Guhao Feng, Wei Xiong, Li Zhao, Di He, Jiang Bian, Liwei Wang:
DPO Meets PPO: Reinforced Token Optimization for RLHF. CoRR abs/2404.18922 (2024) - 2023
- [j1]Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan:
Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers? J. Mach. Learn. Res. 24: 35:1-35:52 (2023) - [c15]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Liwei Wang, Tong Zhang:
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game. ICLR 2023 - [c14]Jiachen Hu, Han Zhong, Chi Jin, Liwei Wang:
Provable Sim-to-real Transfer in Continuous Domain with Partial Observations. ICLR 2023 - [c13]Han Zhong, Tong Zhang:
A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes. NeurIPS 2023 - [c12]Jose H. Blanchet, Miao Lu, Tong Zhang, Han Zhong:
Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage. NeurIPS 2023 - [c11]Jiayi Huang, Han Zhong, Liwei Wang, Lin Yang:
Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds. NeurIPS 2023 - [c10]Zhihan Liu, Miao Lu, Wei Xiong, Han Zhong, Hao Hu, Shenao Zhang, Sirui Zheng, Zhuoran Yang, Zhaoran Wang:
Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration. NeurIPS 2023 - [c9]Shuang Qiu, Ziyu Dai, Han Zhong, Zhaoran Wang, Zhuoran Yang, Tong Zhang:
Posterior Sampling for Competitive RL: Function Approximation and Partial Observation. NeurIPS 2023 - [c8]Yunchang Yang, Han Zhong, Tianhao Wu, Bin Liu, Liwei Wang, Simon S. Du:
A Reduction-based Framework for Sequential Decision Making with Delayed Feedback. NeurIPS 2023 - [i23]Yunchang Yang, Han Zhong, Tianhao Wu, Bin Liu, Liwei Wang, Simon S. Du:
A Reduction-based Framework for Sequential Decision Making with Delayed Feedback. CoRR abs/2302.01477 (2023) - [i22]Han Zhong, Jiachen Hu, Yecheng Xue, Tongyang Li, Liwei Wang:
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret. CoRR abs/2302.10796 (2023) - [i21]Han Zhong, Tong Zhang:
A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes. CoRR abs/2305.08841 (2023) - [i20]Jose H. Blanchet, Miao Lu, Tong Zhang, Han Zhong:
Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage. CoRR abs/2305.09659 (2023) - [i19]Zhihan Liu, Miao Lu, Wei Xiong, Han Zhong, Hao Hu, Shenao Zhang, Sirui Zheng, Zhuoran Yang, Zhaoran Wang:
One Objective to Rule Them All: A Maximization Objective Fusing Estimation and Planning for Exploration. CoRR abs/2305.18258 (2023) - [i18]Jiayi Huang, Han Zhong, Liwei Wang, Lin F. Yang:
Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds. CoRR abs/2306.06836 (2023) - [i17]Rui Yang, Han Zhong, Jiawei Xu, Amy Zhang, Chongjie Zhang, Lei Han, Tong Zhang:
Towards Robust Offline Reinforcement Learning under Diverse Data Corruption. CoRR abs/2310.12955 (2023) - [i16]Shuang Qiu, Ziyu Dai, Han Zhong, Zhaoran Wang, Zhuoran Yang, Tong Zhang:
Posterior Sampling for Competitive RL: Function Approximation and Partial Observation. CoRR abs/2310.19861 (2023) - [i15]Jiayi Huang, Han Zhong, Liwei Wang, Lin F. Yang:
Horizon-Free and Instance-Dependent Regret Bounds for Reinforcement Learning with General Function Approximation. CoRR abs/2312.04464 (2023) - [i14]Wei Xiong, Hanze Dong, Chenlu Ye, Han Zhong, Nan Jiang, Tong Zhang:
Gibbs Sampling from Human Feedback: A Provable KL- constrained Framework for RLHF. CoRR abs/2312.11456 (2023) - 2022
- [c7]Yunchang Yang, Tianhao Wu, Han Zhong, Evrard Garcelon, Matteo Pirotta, Alessandro Lazaric, Liwei Wang, Simon Shaolei Du:
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning. ICLR 2022 - [c6]Xiaoyu Chen, Han Zhong, Zhuoran Yang, Zhaoran Wang, Liwei Wang:
Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation. ICML 2022: 3773-3793 - [c5]Tianhao Wu, Yunchang Yang, Han Zhong, Liwei Wang, Simon S. Du, Jiantao Jiao:
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee. ICML 2022: 24243-24265 - [c4]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Tong Zhang:
A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games. ICML 2022: 24496-24523 - [c3]Han Zhong, Wei Xiong, Jiyuan Tan
, Liwei Wang, Tong Zhang, Zhaoran Wang, Zhuoran Yang:
Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets. ICML 2022: 27117-27142 - [c2]Binghui Li, Jikai Jin, Han Zhong, John E. Hopcroft, Liwei Wang:
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power. NeurIPS 2022 - [i13]Han Zhong, Wei Xiong, Jiyuan Tan, Liwei Wang, Tong Zhang, Zhaoran Wang, Zhuoran Yang:
Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets. CoRR abs/2202.07511 (2022) - [i12]Xiaoyu Chen, Han Zhong, Zhuoran Yang, Zhaoran Wang, Liwei Wang:
Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation. CoRR abs/2205.11140 (2022) - [i11]Binghui Li, Jikai Jin, Han Zhong, John E. Hopcroft, Liwei Wang:
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power. CoRR abs/2205.13863 (2022) - [i10]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Liwei Wang, Tong Zhang:
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game. CoRR abs/2205.15512 (2022) - [i9]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Tong Zhang:
A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games. CoRR abs/2210.01907 (2022) - [i8]Jiachen Hu, Han Zhong, Chi Jin, Liwei Wang:
Provable Sim-to-real Transfer in Continuous Domain with Partial Observations. CoRR abs/2210.15598 (2022) - [i7]Han Zhong, Wei Xiong, Sirui Zheng, Liwei Wang, Zhaoran Wang, Zhuoran Yang, Tong Zhang:
GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond. CoRR abs/2211.01962 (2022) - 2021
- [c1]Han Zhong, Jiayi Huang, Lin Yang, Liwei Wang:
Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs. NeurIPS 2021: 15710-15720 - [i6]Yunchang Yang, Tianhao Wu, Han Zhong, Evrard Garcelon, Matteo Pirotta, Alessandro Lazaric, Liwei Wang, Simon S. Du:
A Unified Framework for Conservative Exploration. CoRR abs/2106.11692 (2021) - [i5]Han Zhong, Zhuoran Yang, Zhaoran Wang, Csaba Szepesvári:
Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs. CoRR abs/2110.08984 (2021) - [i4]Han Zhong, Jiayi Huang, Lin F. Yang, Liwei Wang:
Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs. CoRR abs/2110.13876 (2021) - [i3]Tianhao Wu, Yunchang Yang, Han Zhong, Liwei Wang, Simon S. Du, Jiantao Jiao:
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee. CoRR abs/2112.10935 (2021) - [i2]Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan:
Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopic Followers? CoRR abs/2112.13521 (2021) - 2020
- [i1]Han Zhong, Ethan X. Fang, Zhuoran Yang, Zhaoran Wang:
Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy. CoRR abs/2012.14098 (2020)
Coauthor Index
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