Dongruo Zhou

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 79

Top Topics

Function Approximation

Markov Decision Process

Reinforcement Learning

Top Venues

Publications

Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu
Risk Bounds of Accelerated SGD for Overparameterized Linear Regression. ICLR (2024)
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)
Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. CoRR (2024)
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 (2024)
Zhiyong Wang, Jize Xie, Yi Chen, John C. S. Lui, Dongruo Zhou
Variance-Dependent Regret Bounds for Non-stationary Linear Bandits. CoRR (2024)
Junkai Zhang, Weitong Zhang, Dongruo Zhou, Quanquan Gu
Uncertainty-Aware Reward-Free Exploration with General Function Approximation. CoRR (2024)
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)
Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. ICML (2023)
Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu
Risk Bounds of Accelerated SGD for Overparameterized Linear Regression. CoRR (2023)
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 (2023)
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)
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)
Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. ICML (2023)
Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu
Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits. ICML (2023)
Yue Wu, Dongruo Zhou, Quanquan Gu
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. AISTATS (2022)
Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu
Bandit Learning with General Function Classes: Heteroscedastic Noise and Variance-dependent Regret Bounds. CoRR (2022)
Zixiang Chen, Dongruo Zhou, Quanquan Gu
Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games. ALT (2022)
Dongruo Zhou, Quanquan Gu
Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization. ICML (2022)
Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan
Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium. CoRR (2022)
Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, Hongning Wang
Learning Neural Contextual Bandits through Perturbed Rewards. ICLR (2022)
Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions. NeurIPS (2022)
Dongruo Zhou, Quanquan Gu
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs. NeurIPS (2022)
Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. CoRR (2022)
Zixiang Chen, Dongruo Zhou, Quanquan Gu
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima. ALT (2022)
Dongruo Zhou, Quanquan Gu
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs. CoRR (2022)
Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan
Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium. NeurIPS (2022)
Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions. CoRR (2022)
Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, Hongning Wang
Learning Contextual Bandits Through Perturbed Rewards. CoRR (2022)
Jiafan He, Dongruo Zhou, Quanquan Gu
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs. AISTATS (2022)
Jiafan He, Dongruo Zhou, Quanquan Gu
Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation. CoRR (2021)
Tianhao Wang, Dongruo Zhou, Quanquan Gu
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints. CoRR (2021)
Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang, Quanquan Gu, Rebecca Willett, Robert Nowak
Pure Exploration in Kernel and Neural Bandits. NeurIPS (2021)
Jiafan He, Dongruo Zhou, Quanquan Gu
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation. NeurIPS (2021)
Dongruo Zhou, Quanquan Gu, Csaba Szepesvári
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes. COLT (2021)
Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu
Variance-Aware Off-Policy Evaluation with Linear Function Approximation. NeurIPS (2021)
Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu
Variance-Aware Off-Policy Evaluation with Linear Function Approximation. CoRR (2021)
Heyang Zhao, Dongruo Zhou, Quanquan Gu
Linear Contextual Bandits with Adversarial Corruptions. CoRR (2021)
Weitong Zhang, Dongruo Zhou, Quanquan Gu
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation. NeurIPS (2021)
Jiafan He, Dongruo Zhou, Quanquan Gu
Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs. NeurIPS (2021)
Tianhao Wang, Dongruo Zhou, Quanquan Gu
Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints. NeurIPS (2021)
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)
Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu
Neural Thompson Sampling. ICLR (2021)
Yue Wu, Dongruo Zhou, Quanquan Gu
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. CoRR (2021)
Jiafan He, Dongruo Zhou, Quanquan Gu
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. ICML (2021)
Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu
Provably Efficient Representation Learning in Low-rank Markov Decision Processes. CoRR (2021)
Zixiang Chen, Dongruo Zhou, Quanquan Gu
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima. CoRR (2021)
Dongruo Zhou, Jiafan He, Quanquan Gu
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. ICML (2021)
Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang, Quanquan Gu, Rebecca Willett, Robert D. Nowak
Pure Exploration in Kernel and Neural Bandits. CoRR (2021)
Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu
Iterative Teacher-Aware Learning. CoRR (2021)
Zixiang Chen, Dongruo Zhou, Quanquan Gu
Almost Optimal Algorithms for Two-player Markov Games with Linear Function Approximation. CoRR (2021)
Weitong Zhang, Dongruo Zhou, Quanquan Gu
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation. CoRR (2021)
Jiafan He, Dongruo Zhou, Quanquan Gu
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation. CoRR (2021)
Quanquan Gu, Amin Karbasi, Khashayar Khosravi, Vahab S. Mirrokni, Dongruo Zhou
Batched Neural Bandits. CoRR (2021)
Jinghui Chen, Dongruo Zhou, Jinfeng Yi, Quanquan Gu
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks. AAAI (2020)
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)
Dongruo Zhou, Quanquan Gu
Stochastic Recursive Variance-Reduced Cubic Regularization Methods. AISTATS (2020)
Dongruo Zhou, Jiahao Chen, Quanquan Gu
Provable Multi-Objective Reinforcement Learning with Generative Models. CoRR (2020)
Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu
Gradient descent optimizes over-parameterized deep ReLU networks. Mach. Learn. 109 (3) (2020)
Dongruo Zhou, Yuan Cao, Quanquan Gu
Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization. AISTATS (2020)
Dongruo Zhou, Pan Xu, Quanquan Gu
Stochastic Nested Variance Reduction for Nonconvex Optimization. J. Mach. Learn. Res. 21 (2020)
Jiafan He, Dongruo Zhou, Quanquan Gu
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. CoRR (2020)
Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu
Neural Thompson Sampling. CoRR (2020)
Dongruo Zhou, Lihong Li, Quanquan Gu
Neural Contextual Bandits with UCB-based Exploration. ICML (2020)
Dongruo Zhou, Quanquan Gu, Csaba Szepesvári
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes. CoRR (2020)
Dongruo Zhou, Jiafan He, Quanquan Gu
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. CoRR (2020)
Jiafan He, Dongruo Zhou, Quanquan Gu
Minimax Optimal Reinforcement Learning for Discounted MDPs. CoRR (2020)
Dongruo Zhou, Quanquan Gu
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization. ICML (2019)
Dongruo Zhou, Quanquan Gu
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization. CoRR (2019)
Dongruo Zhou, Pan Xu, Quanquan Gu
Stochastic Variance-Reduced Cubic Regularization Methods. J. Mach. Learn. Res. 20 (2019)
Dongruo Zhou, Lihong Li, Quanquan Gu
Neural Contextual Bandits with Upper Confidence Bound-Based Exploration. CoRR (2019)
Dongruo Zhou, Quanquan Gu
Stochastic Recursive Variance-Reduced Cubic Regularization Methods. CoRR (2019)
Dongruo Zhou, Pan Xu, Quanquan Gu
Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization. NeurIPS (2018)
Dongruo Zhou, Pan Xu, Quanquan Gu
Stochastic Nested Variance Reduction for Nonconvex Optimization. CoRR (2018)
Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks. CoRR (2018)
Dongruo Zhou, Pan Xu, Quanquan Gu
Stochastic Variance-Reduced Cubic Regularized Newton Method. ICML (2018)
Dongruo Zhou, Pan Xu, Quanquan Gu
Sample Efficient Stochastic Variance-Reduced Cubic Regularization Method. CoRR (2018)
Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. CoRR (2018)
Dongruo Zhou, Pan Xu, Quanquan Gu
Stochastic Variance-Reduced Cubic Regularized Newton Method. CoRR (2018)
Dongruo Zhou, Pan Xu, Quanquan Gu
Finding Local Minima via Stochastic Nested Variance Reduction. CoRR (2018)