| 2025 | ICML | Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback. | Qiwei Di, Jiafan He, Quanquan Gu |
| 2024 | ICLR | Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning. | Qiwei Di, Heyang Zhao, Jiafan He, Quanquan Gu |
| 2024 | ICLR | Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs. | Kaixuan Ji, Qingyue Zhao, Jiafan He, Weitong Zhang, Quanquan Gu |
| 2024 | ICML | Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption. | Chenlu Ye, Jiafan He, Quanquan Gu, Tong Zhang |
| 2024 | INFOCOM | Emergency Localization for Mobile Ground Users: An Adaptive UAV Trajectory Planning Method. | Zhihao Zhu, Jiafan He, Luyang Hou, Lianming Xu, Wendi Zhu, Li Wang |
| 2023 | COLT | Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency. | Heyang Zhao, Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu |
| 2023 | ICML | Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. | Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu |
| 2023 | ICML | Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. | Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu |
| 2023 | ICML | Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation. | Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu |
| 2023 | ICML | On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits. | Weitong Zhang, Jiafan He, Zhiyuan Fan, Quanquan Gu |
| 2023 | ICML | Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits. | Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu |
| 2023 | UAI | Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension. | Yue Wu, Jiafan He, Quanquan Gu |
| 2023 | UAI | Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL. | Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu |
| 2022 | ACML | Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes. | Chonghua Liao, Jiafan He, Quanquan Gu |
| 2022 | AISTATS | Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs. | Jiafan He, Dongruo Zhou, Quanquan Gu |
| 2022 | ICML | On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs. | Yuanzhou Chen, Jiafan He, Quanquan Gu |
| 2022 | ICML | Learning Stochastic Shortest Path with Linear Function Approximation. | Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu |
| 2021 | ICML | Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. | Jiafan He, Dongruo Zhou, Quanquan Gu |
| 2021 | ICML | Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. | Dongruo Zhou, Jiafan He, Quanquan Gu |
| 2019 | IJCAI | Achieving a Fairer Future by Changing the Past. | Jiafan He, Ariel D. Procaccia, Alexandros Psomas, David Zeng |