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Han Zhong

30 papers · 2021–2026 · 6 venues · across top CS/AI venues

Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.

Achievements

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+10 more ↓ πŸ—ΊοΈ Taxonomy Completionist (47) πŸƒ Academic Marathon Β· Sprint 🌍 Conference Polyglot Β· Silver πŸŒ‰ Interdisciplinary Bridge Β· Silver 🐣 Hot Topic Early Bird
🀝 Dynamic Duo (15) πŸ† Keyword Champion (2) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (13) ⚑ Prolific Year Β· Prolific πŸ”₯ Unstoppable Β· Bronze ❓ The Questioner πŸ’Ž Century Club Β· Bronze 🌈 Renaissance Researcher (5) πŸ—ƒοΈ Keyword Collector (140)

Venues

ICML (12) NeurIPS (10) ICLR (5) AISTATS (1) COLT (1) JMLR (1)

Top co-authors

Liwei Wang (15) Tong Zhang (9) Zhaoran Wang (7) Zhuoran Yang (7) Wei Xiong (6) Jiachen Hu (3) Miao Lu (3) Tianhao Wu (3) Yunchang Yang (3) Lin Yang (3)

Research topics

Machine Learning (26) Reinforcement Learning (22) Methods (22) Optimization & Theory (20) Artificial Intelligence (16) Core AI (14) Learning Types (12) Mathematics & Optimization (4) Optimization (4) Deep Learning (2) Application Areas (2) Bayesian & Probabilistic (2) Data Science & Analytics (1) Natural Language Processing (1) Applications (1)

Keywords

regret bound (12) markov game (6) reinforcement learning (6) function approximation (5) offline reinforcement learning (4) multi-armed bandit (4) linear function approximation (4) linear bandit (3) sample complexity (3) robust statistics (2) multi-agent reinforcement learning (2) partial observation (2) generalized eluder coefficient (2) sample efficiency (2) policy optimization (2) posterior sampling (2) markov decision process (2) model-based reinforcement learning (2) proximal policy optimization (2) direct preference optimization (1)

Papers

Optimism Stabilizes Thompson Sampling for Adaptive Inference COLT 2026 BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning ICML 2025 DPO Meets PPO: Reinforced Token Optimization for RLHF ICML 2025 The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability ICML 2025 A3S: A General Active Clustering Method with Pairwise Constraints ICML 2024 Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond ICML 2024 Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms NeurIPS 2024 Horizon-Free and Instance-Dependent Regret Bounds for Reinforcement Learning with General Function Approximation AISTATS 2024 Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint ICML 2024 Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret ICML 2024 Rethinking Model-based, Policy-based, and Value-based Reinforcement Learning via the Lens of Representation Complexity NeurIPS 2024 Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment ICML 2024 Sample-efficient Learning of Infinite-horizon Average-reward MDPs with General Function Approximation ICLR 2024 Towards Robust Offline Reinforcement Learning under Diverse Data Corruption ICLR 2024 A Reduction-based Framework for Sequential Decision Making with Delayed Feedback NeurIPS 2023 A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes NeurIPS 2023 Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers? JMLR 2023 Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage NeurIPS 2023 Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration NeurIPS 2023 Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game ICLR 2023 Posterior Sampling for Competitive RL: Function Approximation and Partial Observation NeurIPS 2023 Provable Sim-to-real Transfer in Continuous Domain with Partial Observations ICLR 2023 Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds NeurIPS 2023 A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning ICLR 2022 A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games ICML 2022 Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation ICML 2022 Nearly Optimal Policy Optimization with Stable at Any Time Guarantee ICML 2022 Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets ICML 2022 Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power NeurIPS 2022 Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs NeurIPS 2021