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Dongruo Zhou
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2020 – today
- 2024
- [j4]Dongruo Zhou, Jinghui Chen, Yuan Cao, Ziyan Yang, Quanquan Gu:
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. Trans. Mach. Learn. Res. 2024 (2024) - [c36]Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu:
Risk Bounds of Accelerated SGD for Overparameterized Linear Regression. ICLR 2024 - [c35]Junkai Zhang, Weitong Zhang, Dongruo Zhou, Quanquan Gu:
Uncertainty-Aware Reward-Free Exploration with General Function Approximation. ICML 2024 - [c34]Zihao Wang, Rui Zhu, Dongruo Zhou, Zhikun Zhang, John Mitchell, Haixu Tang, XiaoFeng Wang:
DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training. USENIX Security Symposium 2024 - [i41]Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. CoRR abs/2402.08998 (2024) - [i40]Zihao Wang, Rui Zhu, Dongruo Zhou
, Zhikun Zhang, John Mitchell, Haixu Tang, Xiaofeng Wang:
DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training. CoRR abs/2403.02571 (2024) - [i39]Zhiyong Wang, Jize Xie, Yi Chen, John C. S. Lui, Dongruo Zhou:
Variance-Dependent Regret Bounds for Non-stationary Linear Bandits. CoRR abs/2403.10732 (2024) - [i38]Junkai Zhang, Weitong Zhang, Dongruo Zhou, Quanquan Gu:
Uncertainty-Aware Reward-Free Exploration with General Function Approximation. CoRR abs/2406.16255 (2024) - [i37]Zhiyong Wang, Dongruo Zhou, John C. S. Lui, Wen Sun:
Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds. CoRR abs/2408.08994 (2024) - 2023
- [b1]Dongruo Zhou:
Efficient Reinforcement Learning through Uncertainties. University of California, Los Angeles, USA, 2023 - [c33]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 - [c32]Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. ICML 2023: 7837-7864 - [c31]Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. ICML 2023: 12790-12822 - [c30]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 - [c29]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 - [i36]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) - [i35]Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu:
Risk Bounds of Accelerated SGD for Overparameterized Linear Regression. CoRR abs/2311.14222 (2023) - 2022
- [c28]Yue Wu, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. AISTATS 2022: 3883-3913 - [c27]Jiafan He, Dongruo Zhou, Quanquan Gu:
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs. AISTATS 2022: 4259-4280 - [c26]Zixiang Chen, Dongruo Zhou, Quanquan Gu:
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima. ALT 2022: 176-204 - [c25]Zixiang Chen, Dongruo Zhou, Quanquan Gu:
Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games. ALT 2022: 227-261 - [c24]Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, Hongning Wang:
Learning Neural Contextual Bandits through Perturbed Rewards. ICLR 2022 - [c23]Dongruo Zhou, Quanquan Gu:
Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization. ICML 2022: 27143-27158 - [c22]Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu:
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions. NeurIPS 2022 - [c21]Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan:
Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium. NeurIPS 2022 - [c20]Dongruo Zhou, Quanquan Gu:
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs. NeurIPS 2022 - [i34]Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, Hongning Wang:
Learning Contextual Bandits Through Perturbed Rewards. CoRR abs/2201.09910 (2022) - [i33]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) - [i32]Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu:
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions. CoRR abs/2205.06811 (2022) - [i31]Dongruo Zhou, Quanquan Gu:
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs. CoRR abs/2205.11507 (2022) - [i30]Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan:
Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium. CoRR abs/2208.05363 (2022) - [i29]Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. CoRR abs/2212.06132 (2022) - 2021
- [c19]Dongruo Zhou, Quanquan Gu, Csaba Szepesvári:
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes. COLT 2021: 4532-4576 - [c18]Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu:
Neural Thompson Sampling. ICLR 2021 - [c17]Jiafan He, Dongruo Zhou, Quanquan Gu:
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. ICML 2021: 4171-4180 - [c16]Dongruo Zhou, Jiafan He, Quanquan Gu:
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. ICML 2021: 12793-12802 - [c15]Weitong Zhang, Dongruo Zhou, Quanquan Gu:
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation. NeurIPS 2021: 1582-1593 - [c14]Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu:
Variance-Aware Off-Policy Evaluation with Linear Function Approximation. NeurIPS 2021: 7598-7610 - [c13]Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang, Quanquan Gu, Rebecca Willett, Robert Nowak:
Pure Exploration in Kernel and Neural Bandits. NeurIPS 2021: 11618-11630 - [c12]Tianhao Wang, Dongruo Zhou, Quanquan Gu:
Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints. NeurIPS 2021: 13524-13536 - [c11]Jiafan He, Dongruo Zhou, Quanquan Gu:
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation. NeurIPS 2021: 14188-14199 - [c10]Jiafan He, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs. NeurIPS 2021: 22288-22300 - [c9]Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu:
Iterative Teacher-Aware Learning. NeurIPS 2021: 29231-29245 - [i28]Tianhao Wang, Dongruo Zhou, Quanquan Gu:
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints. CoRR abs/2101.02195 (2021) - [i27]Yue Wu, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. CoRR abs/2102.07301 (2021) - [i26]Zixiang Chen, Dongruo Zhou, Quanquan Gu:
Almost Optimal Algorithms for Two-player Markov Games with Linear Function Approximation. CoRR abs/2102.07404 (2021) - [i25]Jiafan He, Dongruo Zhou, Quanquan Gu:
Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation. CoRR abs/2102.08940 (2021) - [i24]Quanquan Gu, Amin Karbasi, Khashayar Khosravi, Vahab S. Mirrokni, Dongruo Zhou:
Batched Neural Bandits. CoRR abs/2102.13028 (2021) - [i23]Jiafan He, Dongruo Zhou, Quanquan Gu:
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation. CoRR abs/2106.11612 (2021) - [i22]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) - [i21]Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu:
Variance-Aware Off-Policy Evaluation with Linear Function Approximation. CoRR abs/2106.11960 (2021) - [i20]Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang, Quanquan Gu, Rebecca Willett, Robert D. Nowak:
Pure Exploration in Kernel and Neural Bandits. CoRR abs/2106.12034 (2021) - [i19]Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu:
Iterative Teacher-Aware Learning. CoRR abs/2110.00137 (2021) - [i18]Weitong Zhang, Dongruo Zhou, Quanquan Gu:
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation. CoRR abs/2110.06394 (2021) - [i17]Heyang Zhao, Dongruo Zhou, Quanquan Gu:
Linear Contextual Bandits with Adversarial Corruptions. CoRR abs/2110.12615 (2021) - [i16]Zixiang Chen, Dongruo Zhou, Quanquan Gu:
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima. CoRR abs/2110.13144 (2021) - 2020
- [j3]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Nested Variance Reduction for Nonconvex Optimization. J. Mach. Learn. Res. 21: 103:1-103:63 (2020) - [j2]Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu
Gradient descent optimizes over-parameterized deep ReLU networks. Mach. Learn. 109(3): 467-492 (2020) - [c8]Jinghui Chen, Dongruo Zhou, Jinfeng Yi, Quanquan Gu:
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks. AAAI 2020: 3486-3494 - [c7]Dongruo Zhou, Quanquan Gu:
Stochastic Recursive Variance-Reduced Cubic Regularization Methods. AISTATS 2020: 3980-3990 - [c6]Dongruo Zhou, Yuan Cao, Quanquan Gu:
Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization. AISTATS 2020: 4430-4440 - [c5]Dongruo Zhou, Lihong Li, Quanquan Gu:
Neural Contextual Bandits with UCB-based Exploration. ICML 2020: 11492-11502 - [c4]Jinghui Chen, Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu:
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. IJCAI 2020: 3267-3275 - [i15]Dongruo Zhou, Jiafan He, Quanquan Gu:
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. CoRR abs/2006.13165 (2020) - [i14]Jiafan He, Dongruo Zhou, Quanquan Gu:
Minimax Optimal Reinforcement Learning for Discounted MDPs. CoRR abs/2010.00587 (2020) - [i13]Weitong Zhang
Neural Thompson Sampling. CoRR abs/2010.00827 (2020) - [i12]Dongruo Zhou, Jiahao Chen, Quanquan Gu:
Provable Multi-Objective Reinforcement Learning with Generative Models. CoRR abs/2011.10134 (2020) - [i11]Jiafan He, Dongruo Zhou, Quanquan Gu:
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. CoRR abs/2011.11566 (2020) - [i10]Dongruo Zhou, Quanquan Gu, Csaba Szepesvári:
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes. CoRR abs/2012.08507 (2020)
2010 – 2019
- 2019
- [j1]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Variance-Reduced Cubic Regularization Methods. J. Mach. Learn. Res. 20: 134:1-134:47 (2019) - [c3]Dongruo Zhou, Quanquan Gu:
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization. ICML 2019: 7574-7583 - [i9]Dongruo Zhou, Quanquan Gu:
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization. CoRR abs/1901.11224 (2019) - [i8]Dongruo Zhou, Quanquan Gu:
Stochastic Recursive Variance-Reduced Cubic Regularization Methods. CoRR abs/1901.11518 (2019) - [i7]Dongruo Zhou, Lihong Li, Quanquan Gu:
Neural Contextual Bandits with Upper Confidence Bound-Based Exploration. CoRR abs/1911.04462 (2019) - 2018
- [c2]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Variance-Reduced Cubic Regularized Newton Method. ICML 2018: 5985-5994 - [c1]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization. NeurIPS 2018: 3925-3936 - [i6]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Variance-Reduced Cubic Regularized Newton Method. CoRR abs/1802.04796 (2018) - [i5]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Nested Variance Reduction for Nonconvex Optimization. CoRR abs/1806.07811 (2018) - [i4]Dongruo Zhou, Pan Xu, Quanquan Gu:
Finding Local Minima via Stochastic Nested Variance Reduction. CoRR abs/1806.08782 (2018) - [i3]Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu:
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. CoRR abs/1808.05671 (2018) - [i2]Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu:
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks. CoRR abs/1811.08888 (2018) - [i1]Dongruo Zhou, Pan Xu, Quanquan Gu:
Sample Efficient Stochastic Variance-Reduced Cubic Regularization Method. CoRR abs/1811.11989 (2018)
Coauthor Index
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last updated on 2024-10-21 20:30 CEST by the dblp team
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