Dongruo Zhou

I received my Ph.D. from the University of California, Los Angeles, advised by Prof. Quanquan Gu. Previously I obtained my B.Sc from the Department of Mathematics Science, Tsinghua University. After that, I spent one year at the Department of System and Information Engineering (Engineering Systems and Environment), University of Virginia.

My name is pronounced as DAWN-raw JOH.

Research Interests

My research interests are about foundation of machine learning. Specifically, I am interested in

1. Foundation of sequential decision-making problems, including bandits and reinforcement learning (RL),

   1. Nearly optimal statistical complexity for RL with function approximation [COLT21] [NeurIPS22] [ICML23] [COLT23], 

   2. Provable contextual bandits with neural networks [ICML20] [ICLR21] [NeurIPS21],

2. Foundation of optimization algorithms for deep learning,

   1. Nearly optimal sample complexity for stochastic gradient descent-based algorithms [NeurIPS18] [ICML19],

   2. Provable stochastic optimization algorithms escaping from saddle points [JMLR21] [ALT22].

Recently, I have also been interested in decision-making algorithms for problems with complex structures (e.g., RL for large language model, hierarchal RL) from both algorithmic and systemic aspects. 

Education

• 2018 - 2023 University of California, Los Angeles,

Ph.D. in Computer Science.

• 2017 - 2018 University of Virginia,

Ph.D. student in System and Information Engineering.

• 2013 - 2017 Tsinghua University,

Bachelor of Science in Pure and applied Mathematics.

Publication

Conference Publications

• Risk Bounds of Accelerated SGD for Overparameterized Linear Regression, 

Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu,

International Conference on Learning Representations (ICLR), 2024. 

• DPAdapter: Improving differentially private deep learning through noise tolerance pre-training, 

Zihao Wang*, Rui Zhu*, Dongruo Zhou, Zhikun Zhang, John Mitchell, Haixu Tang, and XiaoFeng Wang,

USENIX, 2024.

• Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL, 

Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu,

Uncertainty in Artificial Intelligence (UAI), 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,

Conference on Learning Theory (COLT), 2023.

• Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path, 

Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu, 

International Conference on Machine Learning (ICML), 2023.

• Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes, 

Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu, 

International Conference on Machine Learning (ICML), 2023.

• Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits, 

Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu, 

International Conference on Machine Learning (ICML), 2023.

• Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions, 

Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu, 

Advances in Neural Information Processing Systems (NeurIPS), 2022.

• Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs,

Dongruo Zhou, Quanquan Gu,

Advances in Neural Information Processing Systems (NeurIPS), 2022; Oral. [code]

• Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium,

Chris Junchi Li*, Dongruo Zhou*, Quanquan Gu, Michael I. Jordan, 

Advances in Neural Information Processing Systems (NeurIPS), 2022.

• Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization,

Dongruo Zhou, Quanquan Gu,

International Conference on Machine Learning (ICML), 2022.

• Learning Neural Contextual Bandits through Perturbed Rewards,

Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, Hongning Wang, 

International Conference on Learning Representations (ICLR), 2022. 

• Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation,

Yue Wu, Dongruo Zhou, Quanquan Gu,

International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.

• Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs,

Jiafan He, Dongruo Zhou, Quanquan Gu,

International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.

• Faster Perturbed Stochastic Gradient Methods for Finding Local Minima,

Zixiang Chen*, Dongruo Zhou*, Quanquan Gu,

Algorithmic Learning Theory (ALT), 2022.

• Almost Optimal Algorithms for Two-player Markov Games with Linear Function Approximation,

Zixiang Chen, Dongruo Zhou, Quanquan Gu,

Algorithmic Learning Theory (ALT), 2022.

• Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation,

Jiafan He, Dongruo Zhou, Quanquan Gu,

Advances in Neural Information Processing Systems (NeurIPS), 2021.

• Pure Exploration in Kernel and Neural Bandits,

Yinglun Zhu*, Dongruo Zhou*, Ruoxi Jiang*, Quanquan Gu, Rebecca Willett, Robert D. Nowak,

Advances in Neural Information Processing Systems (NeurIPS), 2021. [code]

• Variance-Aware Off-Policy Evaluation with Linear Function Approximation,

Yifei Min*, Tianhao Wang*, Dongruo Zhou, Quanquan Gu,

Advances in Neural Information Processing Systems (NeurIPS), 2021.

• Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints,

Tianhao Wang*, Dongruo Zhou*, Quanquan Gu,

Advances in Neural Information Processing Systems (NeurIPS), 2021.

• Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation,

Weitong Zhang, Dongruo Zhou, Quanquan Gu,

Advances in Neural Information Processing Systems (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

Advances in Neural Information Processing Systems (NeurIPS), 2021.

• Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs,

Jiafan He, Dongruo Zhou, Quanquan Gu,

Advances in Neural Information Processing Systems (NeurIPS), 2021.

• Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes,

Dongruo Zhou, Quanquan Gu, Csaba Szepesvári, 

Conference on Learning Theory (COLT), 2021.

• Logarithmic Regret for Reinforcement Learning with Linear Function Approximation,

Jiafan He, Dongruo Zhou, Quanquan Gu,

International Conference on Machine Learning (ICML), 2021.

• Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping,

Dongruo Zhou, Jiafan He, Quanquan Gu,

International Conference on Machine Learning (ICML), 2021. 

• Neural Thompson Sampling,

Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu,

International Conference on Learning Representations (ICLR), 2021. [code]

• Neural Contextual Bandits with UCB-based Exploration,

Dongruo Zhou, Lihong Li, Quanquan Gu,

International Conference on Machine Learning (ICML), 2020. [slides] [code]

• 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,

International Joint Conference on Artificial Intelligence (IJCAI), 2020. [code]

• Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization,

Dongruo Zhou, Yuan Cao, Quanquan Gu, 

International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. 

• Stochastic Recursive Variance-Reduced Cubic Regularization Methods,

Dongruo Zhou, Quanquan Gu, 

International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. 

• A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks,

Jinghui Chen, Dongruo Zhou, Jinfeng Yi, Quanquan Gu, 

Association for the Advancement of Artificial Intelligence (AAAI), 2020.

• Lower Bounds for Smooth Nonconvex Finite-Sum Optimization,

Dongruo Zhou, Quanquan Gu,

International Conference on Machine Learning (ICML), 2019.

• Stochastic Nested Variance Reduction for Nonconvex Optimization,

Dongruo Zhou, Pan Xu, Quanquan Gu,

Advances in Neural Information Processing Systems (NeurIPS), 2018; Spotlight.

• Stochastic Variance-Reduced Cubic Regularized Newton Methods,

Dongruo Zhou, Pan Xu, Quanquan Gu,

International Conference on Machine Learning (ICML), 2018.

Journal Publications

• On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization,

Dongruo Zhou*, Jinghui Chen*, Yuan Cao*, Ziyan Yang, Quanquan Gu.

Transactions on Machine Learning Research (TMLR), 2024.

• Batched Neural Bandits,

Quanquan Gu**, Amin Karbasi**, Khashayar Khosravi**, Vahab Mirrokni**, Dongruo Zhou**,

ACM/IMS Journal of Data Science, 2024.

• Stochastic Nested Variance Reduction for Nonconvex Optimization,

Dongruo Zhou, Pan Xu, Quanquan Gu,

Journal of Machine Learning Research (JMLR), 2020.

• Stochastic Variance-Reduced Cubic Regularized Newton Methods,

Dongruo Zhou, Pan Xu, Quanquan Gu,

Journal of Machine Learning Research (JMLR), 2019.

• Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks,

Difan Zou*, Yuan Cao*, Dongruo Zhou, Quanquan Gu.

Machine Learning Journal (MLJ), 2019. 

Workshop Publications

• Provable Multi-Objective Reinforcement Learning with Generative Models,

Dongruo Zhou, Jiahao Chen, Quanquan Gu,

Challenges of Real-World RL (RWRL), NeurIPS, 2020.