Dongruo Zhou - RankMe

102 papers A* 34A 7B 2Journal 57Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning. Trans. Mach. Learn. Res. Ruhan Wang, Yu Yang, Zhishuai Liu, Dongruo Zhou, Pan Xu
2025	J	`jnl`	Breaking the log(1/Δ CoRR Tianyuan Jin, Qin Zhang, Dongruo Zhou
2025	A*	`conf`	Breaking the log⁡(1/Δ2) Barrier: Better Batched Best Arm Identification with Adaptive Grids. ICLR Tianyuan Jin, Qin Zhang, Dongruo Zhou
2025	A*	`conf`	Federated In-Context Learning: Iterative Refinement for Improved Answer Quality. ICML Ruhan Wang, Zhiyong Wang, Chengkai Huang, Rui Wang, Tong Yu, Lina Yao, John C. S. Lui, Dongruo Zhou
2025	J	`jnl`	Federated In-Context Learning: Iterative Refinement for Improved Answer Quality. CoRR Ruhan Wang, Zhiyong Wang, Chengkai Huang, Rui Wang, Tong Yu, Lina Yao, John C. S. Lui, Dongruo Zhou
2025	J	`jnl`	How to Provably Improve Return Conditioned Supervised Learning? CoRR Zhishuai Liu, Yu Yang, Ruhan Wang, Pan Xu, Dongruo Zhou
2025	J	`jnl`	Instance-Dependent Continuous-Time Reinforcement Learning via Maximum Likelihood Estimation. CoRR Runze Zhao, Yue Yu, Ruhan Wang, Chunfeng Huang, Dongruo Zhou
2025	A*	`conf`	Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds. ICLR Zhiyong Wang, Dongruo Zhou, John C. S. Lui, Wen Sun
2025	J	`jnl`	Near-Optimal Second-Order Guarantees for Model-Based Adversarial Imitation Learning. CoRR Shangzhe Li, Dongruo Zhou, Weitong Zhang
2025	J	`jnl`	On the Limits of Test-Time Compute: Sequential Reward Filtering for Better Inference. CoRR Yue Yu, Qiwei Di, Quanquan Gu, Dongruo Zhou
2025	A*	`conf`	Provable Zero-Shot Generalization in Offline Reinforcement Learning. ICML Zhiyong Wang, Chen Yang, John C. S. Lui, Dongruo Zhou
2025	J	`jnl`	Provable Zero-Shot Generalization in Offline Reinforcement Learning. CoRR Zhiyong Wang, Chen Yang, John C. S. Lui, Dongruo Zhou
2025	—	`conf`	Safe Decision Transformer with Learning-based Constraints. L4DC Ruhan Wang, Dongruo Zhou
2025	A	`conf`	Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation. UAI Runze Zhao, Yue Yu, Adams Yiyue Zhu, Chen Yang, Dongruo Zhou
2025	J	`jnl`	Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation. CoRR Runze Zhao, Yue Yu, Adams Yiyue Zhu, Chen Yang, Dongruo Zhou
2025	A*	`conf`	Sharpness-Aware Initialization: Improving Differentially Private Machine Learning from First Principles. USENIX Security Symposium Zihao Wang, Rui Zhu, Dongruo Zhou, Zhikun Zhang, Xiaofeng Wang, Haixu Tang
2025	J	`jnl`	Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models. CoRR Chengkai Huang, Junda Wu, Yu Xia, Zixu Yu, Ruhan Wang, Tong Yu, Ruiyi Zhang, Ryan A. Rossi, Branislav Kveton, Dongruo Zhou, Julian J. McAuley, Lina Yao
2025	A	`conf`	Variance-Dependent Regret Bounds for Nonstationary Linear Bandits. AISTATS Zhiyong Wang, Jize Xie, Yi Chen, John C. S. Lui, Dongruo Zhou
2024	J	`jnl`	CoPS: Empowering LLM Agents with Provable Cross-Task Experience Sharing. CoRR Chen Yang, Chenyang Zhao, Quanquan Gu, Dongruo Zhou
2024	A*	`conf`	DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training. USENIX Security Symposium Zihao Wang, Rui Zhu, Dongruo Zhou, Zhikun Zhang, John Mitchell, Haixu Tang, Xiaofeng Wang
2024	J	`jnl`	DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training. CoRR Zihao Wang, Rui Zhu, Dongruo Zhou, Zhikun Zhang, John Mitchell, Haixu Tang, Xiaofeng Wang
2024	J	`jnl`	Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds. CoRR Zhiyong Wang, Dongruo Zhou, John C. S. Lui, Wen Sun
2024	J	`jnl`	Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. CoRR Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu
2024	J	`jnl`	On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. Trans. Mach. Learn. Res. Dongruo Zhou, Jinghui Chen, Yuan Cao, Ziyan Yang, Quanquan Gu
2024	J	`jnl`	Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning. CoRR Ruhan Wang, Yu Yang, Zhishuai Liu, Dongruo Zhou, Pan Xu
2024	A*	`conf`	Risk Bounds of Accelerated SGD for Overparameterized Linear Regression. ICLR Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu
2024	A*	`conf`	Uncertainty-Aware Reward-Free Exploration with General Function Approximation. ICML Junkai Zhang, Weitong Zhang, Dongruo Zhou, Quanquan Gu
2024	J	`jnl`	Uncertainty-Aware Reward-Free Exploration with General Function Approximation. CoRR Junkai Zhang, Weitong Zhang, Dongruo Zhou, Quanquan Gu
2024	J	`jnl`	Variance-Dependent Regret Bounds for Non-stationary Linear Bandits. CoRR Zhiyong Wang, Jize Xie, Yi Chen, John C. S. Lui, Dongruo Zhou
2023	—	`—`	Efficient Reinforcement Learning through Uncertainties Dongruo Zhou
2023	A*	`conf`	Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. ICML Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu
2023	A*	`conf`	Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. ICML Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu
2023	A*	`conf`	Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits. ICML Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu
2023	A	`conf`	Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL. UAI Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu
2023	J	`jnl`	Risk Bounds of Accelerated SGD for Overparameterized Linear Regression. CoRR Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu
2023	A*	`conf`	Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency. COLT Heyang Zhao, Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu
2023	J	`jnl`	Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency. CoRR Heyang Zhao, Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu
2022	B	`conf`	Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games. ALT Zixiang Chen, Dongruo Zhou, Quanquan Gu
2022	J	`jnl`	Bandit Learning with General Function Classes: Heteroscedastic Noise and Variance-dependent Regret Bounds. CoRR Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu
2022	A*	`conf`	Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs. NeurIPS Dongruo Zhou, Quanquan Gu
2022	J	`jnl`	Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs. CoRR Dongruo Zhou, Quanquan Gu
2022	A*	`conf`	Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization. ICML Dongruo Zhou, Quanquan Gu
2022	B	`conf`	Faster Perturbed Stochastic Gradient Methods for Finding Local Minima. ALT Zixiang Chen, Dongruo Zhou, Quanquan Gu
2022	J	`jnl`	Learning Contextual Bandits Through Perturbed Rewards. CoRR Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, Hongning Wang
2022	A*	`conf`	Learning Neural Contextual Bandits through Perturbed Rewards. ICLR Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, Hongning Wang
2022	A*	`conf`	Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium. NeurIPS Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan
2022	J	`jnl`	Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium. CoRR Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan
2022	A	`conf`	Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs. AISTATS Jiafan He, Dongruo Zhou, Quanquan Gu
2022	A	`conf`	Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. AISTATS Yue Wu, Dongruo Zhou, Quanquan Gu
2022	J	`jnl`	Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. CoRR Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu
2022	A*	`conf`	Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions. NeurIPS Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu
2022	J	`jnl`	Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions. CoRR Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu
2021	J	`jnl`	Almost Optimal Algorithms for Two-player Markov Games with Linear Function Approximation. CoRR Zixiang Chen, Dongruo Zhou, Quanquan Gu
2021	J	`jnl`	Batched Neural Bandits. CoRR Quanquan Gu, Amin Karbasi, Khashayar Khosravi, Vahab S. Mirrokni, Dongruo Zhou
2021	J	`jnl`	Faster Perturbed Stochastic Gradient Methods for Finding Local Minima. CoRR Zixiang Chen, Dongruo Zhou, Quanquan Gu
2021	A*	`conf`	Iterative Teacher-Aware Learning. NeurIPS Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu
2021	J	`jnl`	Iterative Teacher-Aware Learning. CoRR Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu
2021	J	`jnl`	Linear Contextual Bandits with Adversarial Corruptions. CoRR Heyang Zhao, Dongruo Zhou, Quanquan Gu
2021	A*	`conf`	Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. ICML Jiafan He, Dongruo Zhou, Quanquan Gu
2021	J	`jnl`	Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. CoRR Yue Wu, Dongruo Zhou, Quanquan Gu
2021	A*	`conf`	Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs. NeurIPS Jiafan He, Dongruo Zhou, Quanquan Gu
2021	A*	`conf`	Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes. COLT Dongruo Zhou, Quanquan Gu, Csaba Szepesvári
2021	J	`jnl`	Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation. CoRR Jiafan He, Dongruo Zhou, Quanquan Gu
2021	A*	`conf`	Neural Thompson Sampling. ICLR Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu
2021	A*	`conf`	Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. ICML Dongruo Zhou, Jiafan He, Quanquan Gu
2021	J	`jnl`	Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints. CoRR Tianhao Wang, Dongruo Zhou, Quanquan Gu
2021	A*	`conf`	Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints. NeurIPS Tianhao Wang, Dongruo Zhou, Quanquan Gu
2021	J	`jnl`	Provably Efficient Representation Learning in Low-rank Markov Decision Processes. CoRR Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu
2021	A*	`conf`	Pure Exploration in Kernel and Neural Bandits. NeurIPS Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang, Quanquan Gu, Rebecca Willett, Robert Nowak
2021	J	`jnl`	Pure Exploration in Kernel and Neural Bandits. CoRR Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang, Quanquan Gu, Rebecca Willett, Robert D. Nowak
2021	A*	`conf`	Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation. NeurIPS Weitong Zhang, Dongruo Zhou, Quanquan Gu
2021	J	`jnl`	Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation. CoRR Weitong Zhang, Dongruo Zhou, Quanquan Gu
2021	A*	`conf`	Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation. NeurIPS Jiafan He, Dongruo Zhou, Quanquan Gu
2021	J	`jnl`	Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation. CoRR Jiafan He, Dongruo Zhou, Quanquan Gu
2021	A*	`conf`	Variance-Aware Off-Policy Evaluation with Linear Function Approximation. NeurIPS Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu
2021	J	`jnl`	Variance-Aware Off-Policy Evaluation with Linear Function Approximation. CoRR Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu
2020	A*	`conf`	A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks. AAAI Jinghui Chen, Dongruo Zhou, Jinfeng Yi, Quanquan Gu
2020	A	`conf`	Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization. AISTATS Dongruo Zhou, Yuan Cao, Quanquan Gu
2020	A*	`conf`	Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. IJCAI Jinghui Chen, Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu
2020	J	`jnl`	Gradient descent optimizes over-parameterized deep ReLU networks. Mach. Learn. Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu
2020	J	`jnl`	Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. CoRR Jiafan He, Dongruo Zhou, Quanquan Gu
2020	J	`jnl`	Minimax Optimal Reinforcement Learning for Discounted MDPs. CoRR Jiafan He, Dongruo Zhou, Quanquan Gu
2020	J	`jnl`	Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes. CoRR Dongruo Zhou, Quanquan Gu, Csaba Szepesvári
2020	A*	`conf`	Neural Contextual Bandits with UCB-based Exploration. ICML Dongruo Zhou, Lihong Li, Quanquan Gu
2020	J	`jnl`	Neural Thompson Sampling. CoRR Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu
2020	J	`jnl`	Provable Multi-Objective Reinforcement Learning with Generative Models. CoRR Dongruo Zhou, Jiahao Chen, Quanquan Gu
2020	J	`jnl`	Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. CoRR Dongruo Zhou, Jiafan He, Quanquan Gu
2020	J	`jnl`	Stochastic Nested Variance Reduction for Nonconvex Optimization. J. Mach. Learn. Res. Dongruo Zhou, Pan Xu, Quanquan Gu
2020	A	`conf`	Stochastic Recursive Variance-Reduced Cubic Regularization Methods. AISTATS Dongruo Zhou, Quanquan Gu
2019	A*	`conf`	Lower Bounds for Smooth Nonconvex Finite-Sum Optimization. ICML Dongruo Zhou, Quanquan Gu
2019	J	`jnl`	Lower Bounds for Smooth Nonconvex Finite-Sum Optimization. CoRR Dongruo Zhou, Quanquan Gu
2019	J	`jnl`	Neural Contextual Bandits with Upper Confidence Bound-Based Exploration. CoRR Dongruo Zhou, Lihong Li, Quanquan Gu
2019	J	`jnl`	Stochastic Recursive Variance-Reduced Cubic Regularization Methods. CoRR Dongruo Zhou, Quanquan Gu
2019	J	`jnl`	Stochastic Variance-Reduced Cubic Regularization Methods. J. Mach. Learn. Res. Dongruo Zhou, Pan Xu, Quanquan Gu
2018	J	`jnl`	Finding Local Minima via Stochastic Nested Variance Reduction. CoRR Dongruo Zhou, Pan Xu, Quanquan Gu
2018	J	`jnl`	On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. CoRR Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu
2018	J	`jnl`	Sample Efficient Stochastic Variance-Reduced Cubic Regularization Method. CoRR Dongruo Zhou, Pan Xu, Quanquan Gu
2018	J	`jnl`	Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks. CoRR Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu
2018	A*	`conf`	Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization. NeurIPS Dongruo Zhou, Pan Xu, Quanquan Gu
2018	J	`jnl`	Stochastic Nested Variance Reduction for Nonconvex Optimization. CoRR Dongruo Zhou, Pan Xu, Quanquan Gu
2018	A*	`conf`	Stochastic Variance-Reduced Cubic Regularized Newton Method. ICML Dongruo Zhou, Pan Xu, Quanquan Gu
2018	J	`jnl`	Stochastic Variance-Reduced Cubic Regularized Newton Method. CoRR Dongruo Zhou, Pan Xu, Quanquan Gu