Stochastic Variance-Reduced Cubic Regularized Newton Method. - Dongruo Zhou; Pan Xu 002; Quanquan Gu (ICML, 2018)

Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization. - Dongruo Zhou; Pan Xu 002; Quanquan Gu (NeurIPS, 2018)

Finding Local Minima via Stochastic Nested Variance Reduction. - Dongruo Zhou; Pan Xu 002; Quanquan Gu (CoRR, 2018)

On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. - Dongruo Zhou; Yiqi Tang; Ziyan Yang; Yuan Cao 006; Quanquan Gu (CoRR, 2018)

Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks. - Difan Zou; Yuan Cao 006; Dongruo Zhou; Quanquan Gu (CoRR, 2018)

Sample Efficient Stochastic Variance-Reduced Cubic Regularization Method. - Dongruo Zhou; Pan Xu 002; Quanquan Gu (CoRR, 2018)

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

Neural Contextual Bandits with Upper Confidence Bound-Based Exploration. - Dongruo Zhou; Lihong Li 001; Quanquan Gu (CoRR, 2019)

Stochastic Variance-Reduced Cubic Regularization Methods. - Dongruo Zhou; Pan Xu 002; Quanquan Gu (J. Mach. Learn. Res., 2019)

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

Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization. - Dongruo Zhou; Yuan Cao 006; Quanquan Gu (AISTATS, 2020)

Stochastic Recursive Variance-Reduced Cubic Regularization Methods. - Dongruo Zhou; Quanquan Gu (AISTATS, 2020)

Neural Contextual Bandits with UCB-based Exploration. - Dongruo Zhou; Lihong Li 001; Quanquan Gu (ICML, 2020)

Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. - Jinghui Chen; Dongruo Zhou; Yiqi Tang; Ziyan Yang; Yuan Cao 006; Quanquan Gu (IJCAI, 2020)

Minimax Optimal Reinforcement Learning for Discounted MDPs. - Jiafan He; Dongruo Zhou; Quanquan Gu (CoRR, 2020)

Provable Multi-Objective Reinforcement Learning with Generative Models. - Dongruo Zhou; Jiahao Chen; Quanquan Gu (CoRR, 2020)

Stochastic Nested Variance Reduction for Nonconvex Optimization. - Dongruo Zhou; Pan Xu 002; Quanquan Gu (J. Mach. Learn. Res., 2020)

Gradient descent optimizes over-parameterized deep ReLU networks. - Difan Zou; Yuan Cao 006; Dongruo Zhou; Quanquan Gu (Mach. Learn., 2020)

Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes. - Dongruo Zhou; Quanquan Gu; Csaba Szepesvári (COLT, 2021)

Neural Thompson Sampling. - Weitong Zhang; Dongruo Zhou; Lihong Li 001; Quanquan Gu (ICLR, 2021)

Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. - Jiafan He; Dongruo Zhou; Quanquan Gu (ICML, 2021)

Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. - Dongruo Zhou; Jiafan He; Quanquan Gu (ICML, 2021)

Uniform-PAC Bounds 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 (NeurIPS, 2021)

Variance-Aware Off-Policy Evaluation with Linear Function Approximation. - Yifei Min; Tianhao Wang 002; Dongruo Zhou; Quanquan Gu (NeurIPS, 2021)

Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints. - Tianhao Wang 002; Dongruo Zhou; 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 (NeurIPS, 2021)

Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation. - Weitong Zhang; 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 001 (NeurIPS, 2021)

Almost Optimal Algorithms for Two-player Markov Games with Linear Function Approximation. - Zixiang Chen; Dongruo Zhou; Quanquan Gu (CoRR, 2021)

Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation. - Jiafan He; Dongruo Zhou; Quanquan Gu (CoRR, 2021)

Batched Neural Bandits. - Quanquan Gu; Amin Karbasi; Khashayar Khosravi; Vahab S. Mirrokni; Dongruo Zhou (CoRR, 2021)

Provably Efficient Representation Learning in Low-rank Markov Decision Processes. - Weitong Zhang; Jiafan He; Dongruo Zhou; Amy Zhang 001; Quanquan Gu (CoRR, 2021)

Linear Contextual Bandits with Adversarial Corruptions. - Heyang Zhao; Dongruo Zhou; Quanquan Gu (CoRR, 2021)

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 (AISTATS, 2022)

Faster Perturbed Stochastic Gradient Methods for Finding Local Minima. - Zixiang Chen; Dongruo Zhou; Quanquan Gu (ALT, 2022)

Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games. - Zixiang Chen; Dongruo Zhou; Quanquan Gu (ALT, 2022)

Learning Neural Contextual Bandits through Perturbed Rewards. - Yiling Jia; Weitong Zhang; Dongruo Zhou; Quanquan Gu; Hongning Wang (ICLR, 2022)

Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization. - Dongruo Zhou; Quanquan Gu (ICML, 2022)

Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions. - Jiafan He; Dongruo Zhou; Tong Zhang 001; Quanquan Gu (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)

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

Learning Contextual Bandits Through Perturbed Rewards. - Yiling Jia; Weitong Zhang; Dongruo Zhou; Quanquan Gu; Hongning Wang (CoRR, 2022)

Bandit Learning with General Function Classes: Heteroscedastic Noise and Variance-dependent Regret Bounds. - Heyang Zhao; Dongruo Zhou; Jiafan He; Quanquan Gu (CoRR, 2022)

Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium. - Chris Junchi Li; Dongruo Zhou; Quanquan Gu; Michael I. Jordan (CoRR, 2022)

Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency. - Heyang Zhao; Jiafan He; Dongruo Zhou; Tong Zhang 001; Quanquan Gu (COLT, 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 (ICML, 2023)

Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL. - Weitong Zhang; Jiafan He; Dongruo Zhou; Amy Zhang 001; Quanquan Gu (UAI, 2023)

Efficient Reinforcement Learning through Uncertainties - Dongruo Zhou (2023)

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

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

DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training. - Zihao Wang; Rui Zhu; Dongruo Zhou; Zhikun Zhang 001; John Mitchell; Haixu Tang; XiaoFeng Wang 001 (USENIX Security Symposium, 2024)

Variance-Dependent Regret Bounds for Non-stationary Linear Bandits. - Zhiyong Wang; Jize Xie; Yi Chen; John C. S. Lui; Dongruo Zhou (CoRR, 2024)

CoPS: Empowering LLM Agents with Provable Cross-Task Experience Sharing. - Chen Yang; Chenyang Zhao; Quanquan Gu; Dongruo Zhou (CoRR, 2024)

Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning. - Ruhan Wang; Yu Yang 001; Zhishuai Liu; Dongruo Zhou; Pan Xu 002 (CoRR, 2024)

On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. - Dongruo Zhou; Jinghui Chen; Yuan Cao 006; Ziyan Yang; Quanquan Gu (Trans. Mach. Learn. Res., 2024)

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

Breaking the log⁡(1/Δ2) Barrier: Better Batched Best Arm Identification with Adaptive Grids. - Tianyuan Jin; Qin Zhang 001; 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 (ICLR, 2025)

Safe Decision Transformer with Learning-based Constraints. - Ruhan Wang; Dongruo Zhou (L4DC, 2025)

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

Breaking the log(1/Δ2) Barrier: Better Batched Best Arm Identification with Adaptive Grids. - Tianyuan Jin; Qin Zhang 001; Dongruo Zhou (CoRR, 2025)

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

Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models. - Chengkai Huang; Junda Wu; Yu Xia 007; Zixu Yu; Ruhan Wang; Tong Yu 001; Ruiyi Zhang 002; Ryan A. Rossi; Branislav Kveton; Dongruo Zhou; Julian J. McAuley; Lina Yao 001 (CoRR, 2025)

Federated In-Context Learning: Iterative Refinement for Improved Answer Quality. - Ruhan Wang; Zhiyong Wang; Chengkai Huang; Rui Wang; Tong Yu 001; Lina Yao 001; John C. S. Lui; Dongruo Zhou (CoRR, 2025)

How to Provably Improve Return Conditioned Supervised Learning? - Zhishuai Liu; Yu Yang 001; Ruhan Wang; Pan Xu 002; Dongruo Zhou (CoRR, 2025)

Instance-Dependent Continuous-Time Reinforcement Learning via Maximum Likelihood Estimation. - Runze Zhao; Yue Yu; Ruhan Wang; Chunfeng Huang; Dongruo Zhou (CoRR, 2025)