| 2026 | ACL | Adaptive Test-Time Compute Allocation with Evolving In-Context Demonstrations. | Bowen Zuo, Dongruo Zhou, Yinglun Zhu |
| 2025 | AISTATS | Variance-Dependent Regret Bounds for Nonstationary Linear Bandits. | Zhiyong Wang, Jize Xie, Yi Chen, John C. S. Lui, Dongruo Zhou |
| 2025 | ICLR | Breaking the log(1/Δ2) Barrier: Better Batched Best Arm Identification with Adaptive Grids. | Tianyuan Jin, Qin Zhang, Dongruo Zhou |
| 2025 | ICLR | Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds. | Zhiyong Wang, Dongruo Zhou, John C. S. Lui, Wen Sun |
| 2025 | ICML | 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 |
| 2025 | ICML | Provable Zero-Shot Generalization in Offline Reinforcement Learning. | Zhiyong Wang, Chen Yang, John C. S. Lui, Dongruo Zhou |
| 2025 | UAI | Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation. | Runze Zhao, Yue Yu, Adams Yiyue Zhu, Chen Yang, Dongruo Zhou |
| 2024 | ICLR | Risk Bounds of Accelerated SGD for Overparameterized Linear Regression. | Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu |
| 2024 | ICML | Uncertainty-Aware Reward-Free Exploration with General Function Approximation. | Junkai Zhang, Weitong Zhang, Dongruo Zhou, Quanquan Gu |
| 2023 | COLT | Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency. | Heyang Zhao, Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu |
| 2023 | ICML | Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. | Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu |
| 2023 | ICML | Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. | Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu |
| 2023 | ICML | Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits. | Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu |
| 2023 | UAI | 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 |
| 2022 | AISTATS | Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs. | Jiafan He, Dongruo Zhou, Quanquan Gu |
| 2022 | AISTATS | Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. | Yue Wu, Dongruo Zhou, Quanquan Gu |
| 2022 | ALT | Faster Perturbed Stochastic Gradient Methods for Finding Local Minima. | Zixiang Chen, Dongruo Zhou, Quanquan Gu |
| 2022 | ALT | Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games. | Zixiang Chen, Dongruo Zhou, Quanquan Gu |
| 2022 | ICLR | Learning Neural Contextual Bandits through Perturbed Rewards. | Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, Hongning Wang |
| 2022 | ICML | Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization. | Dongruo Zhou, Quanquan Gu |
| 2021 | COLT | Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes. | Dongruo Zhou, Quanquan Gu, Csaba Szepesvri |
| 2021 | ICLR | Neural Thompson Sampling. | Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu |
| 2021 | ICML | Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. | Jiafan He, Dongruo Zhou, Quanquan Gu |
| 2021 | ICML | Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. | Dongruo Zhou, Jiafan He, Quanquan Gu |
| 2020 | AAAI | A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks. | Jinghui Chen, Dongruo Zhou, Jinfeng Yi, Quanquan Gu |
| 2020 | AISTATS | Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization. | Dongruo Zhou, Yuan Cao, Quanquan Gu |
| 2020 | AISTATS | Stochastic Recursive Variance-Reduced Cubic Regularization Methods. | Dongruo Zhou, Quanquan Gu |
| 2020 | ICML | Neural Contextual Bandits with UCB-based Exploration. | Dongruo Zhou, Lihong Li, Quanquan Gu |
| 2020 | IJCAI | 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 |
| 2019 | ICML | Lower Bounds for Smooth Nonconvex Finite-Sum Optimization. | Dongruo Zhou, Quanquan Gu |
| 2018 | ICML | Stochastic Variance-Reduced Cubic Regularized Newton Method. | Dongruo Zhou, Pan Xu, Quanquan Gu |