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Ruiquan Huang
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2020 – today
- 2024
- [j1]Songtao Feng
, Ming Yin, Ruiquan Huang, Yu-Xiang Wang
, Jing Yang
, Yingbin Liang
:
Toward General Function Approximation in Nonstationary Reinforcement Learning. IEEE J. Sel. Areas Inf. Theory 5: 190-206 (2024) - [c11]Xi Li, Songhe Wang, Ruiquan Huang, Mahanth Gowda, George Kesidis:
Temporal-Distributed Backdoor Attack against Video Based Action Recognition. AAAI 2024: 3199-3207 - [c10]Ruiquan Huang, Yuan Cheng, Jing Yang, Vincent Tan, Yingbin Liang:
Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes. ICLR 2024 - [c9]Ruiquan Huang, Yingbin Liang, Jing Yang:
Provably Efficient UCB-type Algorithms For Learning Predictive State Representations. ICLR 2024 - 2023
- [c8]Xizixiang Wei, Tianhao Wang, Ruiquan Huang, Cong Shen, Jing Yang, H. Vincent Poor:
FLORAS: Differentially Private Wireless Federated Learning Using Orthogonal Sequences. ICC 2023: 3121-3126 - [c7]Yuan Cheng, Ruiquan Huang, Yingbin Liang, Jing Yang:
Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs. ICLR 2023 - [c6]Ruiquan Huang, Jing Yang, Yingbin Liang:
Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-Free RL. ICLR 2023 - [c5]Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang:
Non-stationary Reinforcement Learning under General Function Approximation. ICML 2023: 9976-10007 - [c4]Ruiquan Huang, Huanyu Zhang, Luca Melis, Milan Shen, Meisam Hejazinia, Jing Yang:
Federated Linear Contextual Bandits with User-level Differential Privacy. ICML 2023: 14060-14095 - [c3]Donghao Li, Ruiquan Huang, Cong Shen, Jing Yang:
Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints. ICML 2023: 19527-19564 - [i10]Yuan Cheng, Ruiquan Huang, Jing Yang, Yingbin Liang:
Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs. CoRR abs/2303.10859 (2023) - [i9]Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang:
Non-stationary Reinforcement Learning under General Function Approximation. CoRR abs/2306.00861 (2023) - [i8]Ruiquan Huang, Huanyu Zhang, Luca Melis, Milan Shen, Meisam Hejazinia, Jing Yang:
Federated Linear Contextual Bandits with User-level Differential Privacy. CoRR abs/2306.05275 (2023) - [i7]Donghao Li, Ruiquan Huang, Cong Shen, Jing Yang:
Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints. CoRR abs/2306.06265 (2023) - [i6]Xizixiang Wei, Tianhao Wang, Ruiquan Huang, Cong Shen, Jing Yang, H. Vincent Poor:
Differentially Private Wireless Federated Learning Using Orthogonal Sequences. CoRR abs/2306.08280 (2023) - [i5]Ruiquan Huang, Yingbin Liang, Jing Yang:
Provably Efficient UCB-type Algorithms For Learning Predictive State Representations. CoRR abs/2307.00405 (2023) - [i4]Xi Li, Songhe Wang, Ruiquan Huang, Mahanth Gowda, George Kesidis:
Temporal-Distributed Backdoor Attack Against Video Based Action Recognition. CoRR abs/2308.11070 (2023) - [i3]Ruiquan Huang, Yuan Cheng, Jing Yang, Vincent Tan, Yingbin Liang:
Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes. CoRR abs/2310.13550 (2023) - 2022
- [c2]Duo Cheng, Ruiquan Huang, Cong Shen, Jing Yang:
Cascading Bandits with Two-Level Feedback. ISIT 2022: 1892-1896 - [i2]Ruiquan Huang, Jing Yang, Yingbin Liang:
Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-free RL. CoRR abs/2206.14057 (2022) - 2021
- [c1]Ruiquan Huang, Weiqiang Wu, Jing Yang, Cong Shen:
Federated Linear Contextual Bandits. NeurIPS 2021: 27057-27068 - [i1]Ruiquan Huang, Weiqiang Wu, Jing Yang, Cong Shen:
Federated Linear Contextual Bandits. CoRR abs/2110.14177 (2021)
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