Dongruo Zhou

According to our database, Dongruo Zhou authored at least 61 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization.

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

Trans. Mach. Learn. Res., 2024

Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning.

Ruhan Wang, Yu Yang, Zhishuai Liu, Dongruo Zhou, Pan Xu

CoRR, 2024

CoPS: Empowering LLM Agents with Provable Cross-Task Experience Sharing.

Chen Yang, Chenyang Zhao, Quanquan Gu, Dongruo Zhou

CoRR, 2024

Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds.

Zhiyong Wang, Dongruo Zhou, John C. S. Lui, Wen Sun

CoRR, 2024

Variance-Dependent Regret Bounds for Non-stationary Linear Bandits.

Zhiyong Wang, Jize Xie, Yi Chen, John C. S. Lui, Dongruo Zhou

CoRR, 2024

DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training.

Zihao Wang, Rui Zhu, Dongruo Zhou, Zhikun Zhang, John Mitchell, Haixu Tang, Xiaofeng Wang

CoRR, 2024

DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training.

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

Proceedings of the 33rd USENIX Security Symposium, 2024

Uncertainty-Aware Reward-Free Exploration with General Function Approximation.

Junkai Zhang, Weitong Zhang, Dongruo Zhou, Quanquan Gu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Risk Bounds of Accelerated SGD for Overparameterized Linear Regression.

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Efficient Reinforcement Learning through Uncertainties

Dongruo Zhou

PhD thesis, 2023

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

Proceedings of the Uncertainty in Artificial Intelligence, 2023

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

Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu

Proceedings of the International Conference on Machine Learning, 2023

Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes.

Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu

Proceedings of the International Conference on Machine Learning, 2023

Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path.

Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

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

Bandit Learning with General Function Classes: Heteroscedastic Noise and Variance-dependent Regret Bounds.

Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu

CoRR, 2022

Learning Contextual Bandits Through Perturbed Rewards.

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

CoRR, 2022

Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs.

Dongruo Zhou, Quanquan Gu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions.

Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Dongruo Zhou, Quanquan Gu

Proceedings of the International Conference on Machine Learning, 2022

Learning Neural Contextual Bandits through Perturbed Rewards.

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

Proceedings of the Tenth International Conference on Learning Representations, 2022

Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games.

Zixiang Chen, Dongruo Zhou, Quanquan Gu

Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

Faster Perturbed Stochastic Gradient Methods for Finding Local Minima.

Zixiang Chen, Dongruo Zhou, Quanquan Gu

Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

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

Yue Wu, Dongruo Zhou, Quanquan Gu

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

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

Jiafan He, Dongruo Zhou, Quanquan Gu

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Linear Contextual Bandits with Adversarial Corruptions.

Heyang Zhao, Dongruo Zhou, Quanquan Gu

CoRR, 2021

Provably Efficient Representation Learning in Low-rank Markov Decision Processes.

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

CoRR, 2021

Batched Neural Bandits.

Quanquan Gu, Amin Karbasi, Khashayar Khosravi, Vahab S. Mirrokni, Dongruo Zhou

CoRR, 2021

Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation.

Jiafan He, Dongruo Zhou, Quanquan Gu

CoRR, 2021

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

Zixiang Chen, Dongruo Zhou, Quanquan Gu

CoRR, 2021

Pure Exploration in Kernel and Neural Bandits.

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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation.

Weitong Zhang, Dongruo Zhou, Quanquan Gu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Iterative Teacher-Aware Learning.

Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints.

Tianhao Wang, Dongruo Zhou, Quanquan Gu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Variance-Aware Off-Policy Evaluation with Linear Function Approximation.

Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs.

Jiafan He, Dongruo Zhou, Quanquan Gu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation.

Jiafan He, Dongruo Zhou, Quanquan Gu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping.

Dongruo Zhou, Jiafan He, Quanquan Gu

Proceedings of the 38th International Conference on Machine Learning, 2021

Logarithmic Regret for Reinforcement Learning with Linear Function Approximation.

Jiafan He, Dongruo Zhou, Quanquan Gu

Proceedings of the 38th International Conference on Machine Learning, 2021

Neural Thompson Sampling.

Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu

Proceedings of the 9th International Conference on Learning Representations, 2021

Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes.

Dongruo Zhou, Quanquan Gu, Csaba Szepesvári

Proceedings of the Conference on Learning Theory, 2021

2020

Gradient descent optimizes over-parameterized deep ReLU networks.

Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu

Mach. Learn., 2020

Stochastic Nested Variance Reduction for Nonconvex Optimization.

Dongruo Zhou, Pan Xu, Quanquan Gu

J. Mach. Learn. Res., 2020

Provable Multi-Objective Reinforcement Learning with Generative Models.

Dongruo Zhou, Jiahao Chen, Quanquan Gu

CoRR, 2020

Minimax Optimal Reinforcement Learning for Discounted MDPs.

Jiafan He, Dongruo Zhou, Quanquan Gu

CoRR, 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

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Neural Contextual Bandits with UCB-based Exploration.

Dongruo Zhou, Lihong Li, Quanquan Gu

Proceedings of the 37th International Conference on Machine Learning, 2020

Stochastic Recursive Variance-Reduced Cubic Regularization Methods.

Dongruo Zhou, Quanquan Gu

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization.

Dongruo Zhou, Yuan Cao, Quanquan Gu

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks.

Jinghui Chen, Dongruo Zhou, Jinfeng Yi, Quanquan Gu

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Stochastic Variance-Reduced Cubic Regularization Methods.

Dongruo Zhou, Pan Xu, Quanquan Gu

J. Mach. Learn. Res., 2019

Neural Contextual Bandits with Upper Confidence Bound-Based Exploration.

Dongruo Zhou, Lihong Li, Quanquan Gu

CoRR, 2019

Lower Bounds for Smooth Nonconvex Finite-Sum Optimization.

Dongruo Zhou, Quanquan Gu

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Sample Efficient Stochastic Variance-Reduced Cubic Regularization Method.

Dongruo Zhou, Pan Xu, Quanquan Gu

CoRR, 2018

Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks.

Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu

CoRR, 2018

On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization.

Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu

CoRR, 2018

Finding Local Minima via Stochastic Nested Variance Reduction.

Dongruo Zhou, Pan Xu, Quanquan Gu

CoRR, 2018

Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization.

Dongruo Zhou, Pan Xu, Quanquan Gu

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Stochastic Variance-Reduced Cubic Regularized Newton Method.

Dongruo Zhou, Pan Xu, Quanquan Gu

Proceedings of the 35th International Conference on Machine Learning, 2018