Robust attitude tracking control of a rigid spacecraft based on nonlinearly controlled quaternions. - Dabo Xu; Jiafan He; Andong Sheng; Zhiyong Chen 001; Dan Wang 001 (ASCC, 2017)

Event-Triggered Attitude Regulation of Rigid Spacecraft with Uncertain Inertia Matrix. - Jiafan He; Youfeng Su; Dabo Xu; Andong Sheng (ASCC, 2019)

Rectangular Impulsive Consensus of Multi-agent Systems with Heterogeneous Control Widths. - Pengpeng Ye; Jiafan He; Yinya Li; Guoqing Qi; Andong Sheng (ASCC, 2019)

Robust Attitude Regulation of Uncertain Spacecraft with Flexible Appendages. - Jiafan He; Andong Sheng; Dabo Xu (ICNSC, 2019)

Achieving a Fairer Future by Changing the Past. - Jiafan He; Ariel D. Procaccia; Alexandros Psomas 001; David Zeng (IJCAI, 2019)

Minimax Optimal Reinforcement Learning for Discounted MDPs. - Jiafan He; Dongruo Zhou; Quanquan Gu (CoRR, 2020)

Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. - Jiafan He; Dongruo Zhou; Quanquan Gu (ICML, 2021)

Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. - Dongruo Zhou; Jiafan He; Quanquan Gu (ICML, 2021)

Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation. - Jiafan He; Dongruo Zhou; Quanquan Gu (NeurIPS, 2021)

Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs. - Jiafan He; Dongruo Zhou; Quanquan Gu (NeurIPS, 2021)

Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation. - Jiafan He; Dongruo Zhou; Quanquan Gu (CoRR, 2021)

Provably Efficient Representation Learning in Low-rank Markov Decision Processes. - Weitong Zhang; Jiafan He; Dongruo Zhou; Amy Zhang 001; Quanquan Gu (CoRR, 2021)

Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes. - Chonghua Liao; Jiafan He; Quanquan Gu (ACML, 2022)

Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs. - Jiafan He; Dongruo Zhou; Quanquan Gu (AISTATS, 2022)

Accurate Decision-Making Method for Air Combat Pilots Based on Data-Driven. - Yiming Mao 009; Zhijie Xia; Qingwei Li; Jiafan He; Aiguo Fei (DMBD (2), 2022)

On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs. - Yuanzhou Chen; Jiafan He; Quanquan Gu (ICML, 2022)

Learning Stochastic Shortest Path with Linear Function Approximation. - Yifei Min; Jiafan He; Tianhao Wang 002; Quanquan Gu (ICML, 2022)

A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits. - Jiafan He; Tianhao Wang 002; Yifei Min; Quanquan Gu (NeurIPS, 2022)

Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions. - Jiafan He; Dongruo Zhou; Tong Zhang 001; Quanquan Gu (NeurIPS, 2022)

Bandit Learning with General Function Classes: Heteroscedastic Noise and Variance-dependent Regret Bounds. - Heyang Zhao; Dongruo Zhou; Jiafan He; Quanquan Gu (CoRR, 2022)

Cooperative Global Robust Practical Output Regulation of Nonlinear Lower Triangular Multiagent Systems via Event-Triggered Control. - Jiaqi Wang 005; Wei Xing Zheng 001; Andong Sheng; Jiafan He (IEEE Trans. Cybern., 2022)

Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency. - Heyang Zhao; Jiafan He; Dongruo Zhou; Tong Zhang 001; Quanquan Gu (COLT, 2023)

Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. - Qiwei Di; Jiafan He; Dongruo Zhou; Quanquan Gu (ICML, 2023)

Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. - Jiafan He; Heyang Zhao; Dongruo Zhou; Quanquan Gu (ICML, 2023)

Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation. - Yifei Min; Jiafan He; Tianhao Wang 002; Quanquan Gu (ICML, 2023)

On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits. - Weitong Zhang; Jiafan He; Zhiyuan Fan; Quanquan Gu (ICML, 2023)

Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits. - Heyang Zhao; Dongruo Zhou; Jiafan He; Quanquan Gu (ICML, 2023)

Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension. - Yue Wu; Jiafan He; Quanquan Gu (UAI, 2023)

Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL. - Weitong Zhang; Jiafan He; Dongruo Zhou; Amy Zhang 001; Quanquan Gu (UAI, 2023)

Attitude Synchronization of Heterogenous Flexible Spacecrafts by Measurement-Based Feedback With Disturbance Suppression. - Jiafan He; Aiguo Fei; Qingwei Li; Feng Fang (IEEE Access, 2023)

Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning. - Qiwei Di; Heyang Zhao; Jiafan He; Quanquan Gu (ICLR, 2024)

Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs. - Kaixuan Ji; Qingyue Zhao; Jiafan He; Weitong Zhang; Quanquan Gu (ICLR, 2024)

Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption. - Chenlu Ye; Jiafan He; Quanquan Gu; Tong Zhang 001 (ICML, 2024)

Emergency Localization for Mobile Ground Users: An Adaptive UAV Trajectory Planning Method. - Zhihao Zhu; Jiafan He; Luyang Hou; Lianming Xu; Wendi Zhu; Li Wang (INFOCOM (Workshops), 2024)

Achieving Constant Regret in Linear Markov Decision Processes. - Weitong Zhang; Zhiyuan Fan; Jiafan He; Quanquan Gu (NeurIPS, 2024)

A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation. - Heyang Zhao; Jiafan He; Quanquan Gu (NeurIPS, 2024)

Reinforcement Learning from Human Feedback with Active Queries. - Kaixuan Ji; Jiafan He; Quanquan Gu (CoRR, 2024)

Settling Constant Regrets in Linear Markov Decision Processes. - Weitong Zhang; Zhiyuan Fan; Jiafan He; Quanquan Gu (CoRR, 2024)

Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback. - Qiwei Di; Jiafan He; Quanquan Gu (CoRR, 2024)

Accelerated Preference Optimization for Large Language Model Alignment. - Jiafan He; Huizhuo Yuan; Quanquan Gu (CoRR, 2024)

Multi-Augmentation-Based Contrastive Learning for Semi-Supervised Learning. - Jie Wang; Jie Yang; Jiafan He; Dongliang Peng 001 (Algorithms, 2024)

Variance-Dependent Regret Lower Bounds for Contextual Bandits. - Jiafan He; Quanquan Gu (CoRR, 2025)