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ORCIDJiafan He
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2020 – today
- 2024
[j3]Jie Wang, Jie Yang, Jiafan He, Dongliang Peng:
Multi-Augmentation-Based Contrastive Learning for Semi-Supervised Learning. Algorithms 17(3): 91 (2024)- [c28]Qiwei Di, Heyang Zhao, Jiafan He, Quanquan Gu:
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning. ICLR 2024 - [c27]Kaixuan Ji, Qingyue Zhao, Jiafan He, Weitong Zhang, Quanquan Gu:
Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs. ICLR 2024 - [c26]Chenlu Ye, Jiafan He, Quanquan Gu, Tong Zhang:
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption. ICML 2024 - [c25]Zhihao Zhu, Jiafan He, Luyang Hou, Lianming Xu, Wendi Zhu
, Li Wang:
Emergency Localization for Mobile Ground Users: An Adaptive UAV Trajectory Planning Method. INFOCOM (Workshops) 2024: 1-6 - [i25]Zhihao Zhu, Jiafan He, Luyang Hou, Lianming Xu, Wendi Zhu, Li Wang:
Emergency Localization for Mobile Ground Users: An Adaptive UAV Trajectory Planning Method. CoRR abs/2401.07256 (2024) - [i24]Chenlu Ye, Jiafan He, Quanquan Gu, Tong Zhang:
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption. CoRR abs/2402.08991 (2024) - [i23]Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. CoRR abs/2402.08998 (2024) - [i22]Kaixuan Ji, Jiafan He, Quanquan Gu:
Reinforcement Learning from Human Feedback with Active Queries. CoRR abs/2402.09401 (2024) - [i21]Weitong Zhang, Zhiyuan Fan, Jiafan He, Quanquan Gu:
Settling Constant Regrets in Linear Markov Decision Processes. CoRR abs/2404.10745 (2024) - [i20]Qiwei Di, Jiafan He, Quanquan Gu:
Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback. CoRR abs/2404.10776 (2024) - 2023
- [j2]Jiafan He, Aiguo Fei, Qingwei Li, Feng Fang:
Attitude Synchronization of Heterogenous Flexible Spacecrafts by Measurement-Based Feedback With Disturbance Suppression. IEEE Access 11: 84453-84467 (2023) - [c24]Heyang Zhao, Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu:
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency. COLT 2023: 4977-5020 - [c23]Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. ICML 2023: 7837-7864 - [c22]Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. ICML 2023: 12790-12822 - [c21]Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu:
Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation. ICML 2023: 24785-24811 - [c20]Weitong Zhang, Jiafan He, Zhiyuan Fan, Quanquan Gu:
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits. ICML 2023: 41111-41132 - [c19]Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu:
Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits. ICML 2023: 42259-42279 - [c18]Yue Wu, Jiafan He, Quanquan Gu:
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension. UAI 2023: 2304-2313 - [c17]Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu:
Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL. UAI 2023: 2488-2497 - [i19]Heyang Zhao, Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu:
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency. CoRR abs/2302.10371 (2023) - [i18]Weitong Zhang, Jiafan He, Zhiyuan Fan, Quanquan Gu:
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits. CoRR abs/2303.09390 (2023) - [i17]Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu:
Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation. CoRR abs/2305.06446 (2023) - [i16]Yue Wu, Jiafan He, Quanquan Gu:
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension. CoRR abs/2305.08350 (2023) - [i15]Kaixuan Ji, Qingyue Zhao, Jiafan He, Weitong Zhang, Quanquan Gu:
Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs. CoRR abs/2305.08359 (2023) - [i14]Qiwei Di, Heyang Zhao, Jiafan He, Quanquan Gu:
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning. CoRR abs/2310.01380 (2023) - [i13]Heyang Zhao, Jiafan He, Quanquan Gu:
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation. CoRR abs/2311.15238 (2023) - 2022
- [j1]Jiaqi Wang, Wei Xing Zheng, Andong Sheng, Jiafan He:
Cooperative Global Robust Practical Output Regulation of Nonlinear Lower Triangular Multiagent Systems via Event-Triggered Control. IEEE Trans. Cybern. 52(7): 5708-5719 (2022) - [c16]Chonghua Liao, Jiafan He, Quanquan Gu:
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes. ACML 2022: 627-642 - [c15]Jiafan He, Dongruo Zhou, Quanquan Gu:
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs. AISTATS 2022: 4259-4280 - [c14]Yiming Mao, Zhijie Xia, Qingwei Li, Jiafan He, Aiguo Fei:
Accurate Decision-Making Method for Air Combat Pilots Based on Data-Driven. DMBD (2) 2022: 439-448 - [c13]Yuanzhou Chen, Jiafan He, Quanquan Gu:
On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs. ICML 2022: 3149-3183 - [c12]Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu:
Learning Stochastic Shortest Path with Linear Function Approximation. ICML 2022: 15584-15629 - [c11]Jiafan He, Tianhao Wang, Yifei Min, Quanquan Gu:
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits. NeurIPS 2022 - [c10]Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu:
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions. NeurIPS 2022 - [i12]Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu:
Bandit Learning with General Function Classes: Heteroscedastic Noise and Variance-dependent Regret Bounds. CoRR abs/2202.13603 (2022) - [i11]Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu:
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions. CoRR abs/2205.06811 (2022) - [i10]Jiafan He, Tianhao Wang, Yifei Min, Quanquan Gu:
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits. CoRR abs/2207.03106 (2022) - [i9]Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. CoRR abs/2212.06132 (2022) - 2021
- [c9]Jiafan He, Dongruo Zhou, Quanquan Gu:
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. ICML 2021: 4171-4180 - [c8]Dongruo Zhou, Jiafan He, Quanquan Gu:
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. ICML 2021: 12793-12802 - [c7]Jiafan He, Dongruo Zhou, Quanquan Gu:
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation. NeurIPS 2021: 14188-14199 - [c6]Jiafan He, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs. NeurIPS 2021: 22288-22300 - [i8]Jiafan He, Dongruo Zhou, Quanquan Gu:
Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation. CoRR abs/2102.08940 (2021) - [i7]Jiafan He, Dongruo Zhou, Quanquan Gu:
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation. CoRR abs/2106.11612 (2021) - [i6]Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu:
Provably Efficient Representation Learning in Low-rank Markov Decision Processes. CoRR abs/2106.11935 (2021) - [i5]Chonghua Liao, Jiafan He, Quanquan Gu:
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes. CoRR abs/2110.10133 (2021) - [i4]Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu:
Learning Stochastic Shortest Path with Linear Function Approximation. CoRR abs/2110.12727 (2021) - 2020
- [i3]Dongruo Zhou, Jiafan He, Quanquan Gu:
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. CoRR abs/2006.13165 (2020) - [i2]Jiafan He, Dongruo Zhou, Quanquan Gu:
Minimax Optimal Reinforcement Learning for Discounted MDPs. CoRR abs/2010.00587 (2020) - [i1]Jiafan He, Dongruo Zhou, Quanquan Gu:
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. CoRR abs/2011.11566 (2020)
2010 – 2019
- 2019
- [c5]Pengpeng Ye, Jiafan He, Yinya Li, Guoqing Qi, Andong Sheng:
Rectangular Impulsive Consensus of Multi-agent Systems with Heterogeneous Control Widths. ASCC 2019: 913-918 - [c4]Jiafan He, Youfeng Su, Dabo Xu, Andong Sheng:
Event-Triggered Attitude Regulation of Rigid Spacecraft with Uncertain Inertia Matrix. ASCC 2019: 1661-1665 - [c3]Jiafan He, Andong Sheng, Dabo Xu:
Robust Attitude Regulation of Uncertain Spacecraft with Flexible Appendages. ICNSC 2019: 442-447 - [c2]Jiafan He, Ariel D. Procaccia, Alexandros Psomas, David Zeng:
Achieving a Fairer Future by Changing the Past. IJCAI 2019: 343-349 - 2017
- [c1]Dabo Xu, Jiafan He, Andong Sheng, Zhiyong Chen, Dan Wang:
Robust attitude tracking control of a rigid spacecraft based on nonlinearly controlled quaternions. ASCC 2017: 853-858
Coauthor Index
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last updated on 2024-10-31 21:12 CET by the dblp team
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