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Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency


Feb 21, 2023
Heyang Zhao, Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu

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* 43 pages, 2 tables 

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Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes


Dec 12, 2022
Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu

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* 44 pages, 1 table 

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Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium


Aug 10, 2022
Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan

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* 42 pages 

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Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs


May 23, 2022
Dongruo Zhou, Quanquan Gu

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* 33 pages, 1 table 

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Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions


May 13, 2022
Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu

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* 29 pages, 1 table 

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Bandit Learning with General Function Classes: Heteroscedastic Noise and Variance-dependent Regret Bounds


Feb 28, 2022
Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu

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* 33 pages, 1 table 

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Learning Contextual Bandits Through Perturbed Rewards


Jan 24, 2022
Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, Hongning Wang

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Faster Perturbed Stochastic Gradient Methods for Finding Local Minima


Oct 25, 2021
Zixiang Chen, Dongruo Zhou, Quanquan Gu

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* 28 pages, 1 figure, 1 table 

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Linear Contextual Bandits with Adversarial Corruptions


Oct 25, 2021
Heyang Zhao, Dongruo Zhou, Quanquan Gu

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* 27 pages, 1 figure 

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Iterative Teacher-Aware Learning


Oct 17, 2021
Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu

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* Advances in Neural Information Processing Systems (2021) 

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