Zhou, Dongruo

51 publications

ICLR 2025 Breaking the $\log(1/\Delta_2)$ Barrier: Better Batched Best Arm Identification with Adaptive Grids Tianyuan Jin, Qin Zhang, Dongruo Zhou

ICML 2025 Federated In-Context Learning: Iterative Refinement for Improved Answer Quality Ruhan Wang, Zhiyong Wang, Chengkai Huang, Rui Wang, Tong Yu, Lina Yao, John C.S. Lui, Dongruo Zhou

ICLR 2025 Model-Based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds Zhiyong Wang, Dongruo Zhou, John C.S. Lui, Wen Sun

ICML 2025 Provable Zero-Shot Generalization in Offline Reinforcement Learning Zhiyong Wang, Chen Yang, John C.S. Lui, Dongruo Zhou

L4DC 2025 Safe Decision Transformer with Learning-Based Constraints Ruhan Wang, Dongruo Zhou

UAI 2025 Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Runze Zhao, Yue Yu, Adams Yiyue Zhu, Chen Yang, Dongruo Zhou

AISTATS 2025 Variance-Dependent Regret Bounds for Nonstationary Linear Bandits Zhiyong Wang, Jize Xie, Yi Chen, John C.S. Lui, Dongruo Zhou

NeurIPSW 2024 CoPS: Empowering LLM Agents with Provable Cross-Task Experience Sharing Chen Yang, Chenyang Zhao, Quanquan Gu, Dongruo Zhou

TMLR 2024 On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization Dongruo Zhou, Jinghui Chen, Yuan Cao, Ziyan Yang, Quanquan Gu

ICLR 2024 Risk Bounds of Accelerated SGD for Overparameterized Linear Regression Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu

NeurIPSW 2024 Safe Decision Transformer with Learning-Based Constraints Ruhan Wang, Dongruo Zhou

ICMLW 2024 Towards Zero-Shot Generalization in Offline Reinforcement Learning Zhiyong Wang, Chen Yang, John C.S. Lui, Dongruo Zhou

ICML 2024 Uncertainty-Aware Reward-Free Exploration with General Function Approximation Junkai Zhang, Weitong Zhang, Dongruo Zhou, Quanquan Gu

ICMLW 2024 Variance-Dependent Regret Bounds for Nonstationary Linear Bandits Zhiyong Wang, Jize Xie, Yi Chen, John C.S. Lui, Dongruo Zhou

ICML 2023 Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu

ICML 2023 Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu

ICML 2023 Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu

NeurIPSW 2023 Risk Bounds of Accelerated SGD for Overparameterized Linear Regression Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu

COLT 2023 Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency Heyang Zhao, Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu

AISTATS 2022 Near-Optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs Jiafan He, Dongruo Zhou, Quanquan Gu

AISTATS 2022 Nearly Minimax Optimal Regret for Learning Infinite-Horizon Average-Reward MDPs with Linear Function Approximation Yue Wu, Dongruo Zhou, Quanquan Gu

ALT 2022 Almost Optimal Algorithms for Two-Player Zero-Sum Linear Mixture Markov Games Zixiang Chen, Dongruo Zhou, Quanquan Gu

NeurIPS 2022 Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs Dongruo Zhou, Quanquan Gu

ICML 2022 Dimension-Free Complexity Bounds for High-Order Nonconvex Finite-Sum Optimization Dongruo Zhou, Quanquan Gu

ALT 2022 Faster Perturbed Stochastic Gradient Methods for Finding Local Minima Zixiang Chen, Dongruo Zhou, Quanquan Gu

ICLR 2022 Learning Neural Contextual Bandits Through Perturbed Rewards Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, Hongning Wang

NeurIPS 2022 Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan

NeurIPS 2022 Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu

NeurIPS 2021 Iterative Teacher-Aware Learning Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu

NeurIPSW 2021 Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael Jordan

ICML 2021 Logarithmic Regret for Reinforcement Learning with Linear Function Approximation Jiafan He, Dongruo Zhou, Quanquan Gu

NeurIPS 2021 Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs Jiafan He, Dongruo Zhou, Quanquan Gu

COLT 2021 Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes Dongruo Zhou, Quanquan Gu, Csaba Szepesvari

ICLR 2021 Neural Thompson Sampling Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu

ICML 2021 Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping Dongruo Zhou, Jiafan He, Quanquan Gu

NeurIPS 2021 Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints Tianhao Wang, Dongruo Zhou, Quanquan Gu

NeurIPS 2021 Pure Exploration in Kernel and Neural Bandits Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang, Quanquan Gu, Rebecca Willett, Robert Nowak

NeurIPS 2021 Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation Weitong Zhang, Dongruo Zhou, Quanquan Gu

NeurIPS 2021 Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation Jiafan He, Dongruo Zhou, Quanquan Gu

NeurIPS 2021 Variance-Aware Off-Policy Evaluation with Linear Function Approximation Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu

AAAI 2020 A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks Jinghui Chen, Dongruo Zhou, Jinfeng Yi, Quanquan Gu

AISTATS 2020 Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization Dongruo Zhou, Yuan Cao, Quanquan Gu

IJCAI 2020 Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks Jinghui Chen, Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu

MLJ 2020 Gradient Descent Optimizes Over-Parameterized Deep ReLU Networks Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu

ICML 2020 Neural Contextual Bandits with UCB-Based Exploration Dongruo Zhou, Lihong Li, Quanquan Gu

JMLR 2020 Stochastic Nested Variance Reduction for Nonconvex Optimization Dongruo Zhou, Pan Xu, Quanquan Gu

AISTATS 2020 Stochastic Recursive Variance-Reduced Cubic Regularization Methods Dongruo Zhou, Quanquan Gu

ICML 2019 Lower Bounds for Smooth Nonconvex Finite-Sum Optimization Dongruo Zhou, Quanquan Gu

JMLR 2019 Stochastic Variance-Reduced Cubic Regularization Methods Dongruo Zhou, Pan Xu, Quanquan Gu

NeurIPS 2018 Stochastic Nested Variance Reduction for Nonconvex Optimization Dongruo Zhou, Pan Xu, Quanquan Gu

ICML 2018 Stochastic Variance-Reduced Cubic Regularized Newton Methods Dongruo Zhou, Pan Xu, Quanquan Gu