← Authors

Dongruo Zhou

43 papers · 2018–2025 · 11 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (11) πŸƒ Academic Marathon (7) 🐝 Cross-Pollinator (8) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (35) 🀝 Dynamic Duo (36) πŸ‘‘ Triple Crown πŸ† Grand Slam πŸ”¬ Deep Specialist (13) πŸ† Keyword Champion (2) πŸ—ƒοΈ Keyword Collector (125) ⚑ Prolific Year (6) πŸ”₯ Unstoppable (8) πŸ’Ž Century Club (43)

Conferences

ICML (12) NIPS (11) AISTATS (5) ICLR (5) ALT (2) COLT (2) JMLR (2) AAAI (1) IJCAI (1) L4DC (1) UAI (1)

Top co-authors

Quanquan Gu (36) Jiafan He (10) Weitong ZHANG (4) Pan Xu (4) John C.S. Lui (4) Zhiyong Wang (4) Heyang Zhao (3) Ruhan Wang (2) Yuan Cao (2) Lihong Li (2)

Research topics

Reinforcement Learning (1)

Keywords

regret bound (13) reinforcement learning (9) linear function approximation (8) nonconvex optimization (6) variance reduction (6) stochastic optimization (5) linear mixture mdp (5) minimax optimal (4) stochastic gradient descent (3) markov decision process (3) cubic regularization (3) stochastic gradient (2) contextual bandit (2) multi-armed bandit (2) non-convex optimization (2) online learning (2) reproducing kernel hilbert space (2) sample complexity (2) function approximation (2) finite-sum optimization (2)

Papers

Federated In-Context Learning: Iterative Refinement for Improved Answer Quality ICML 2025
Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds ICLR 2025
Breaking the $\log(1/\Delta_2)$ Barrier: Better Batched Best Arm Identification with Adaptive Grids ICLR 2025
Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation UAI 2025
Safe Decision Transformer with Learning-based Constraints L4DC 2025
Provable Zero-Shot Generalization in Offline Reinforcement Learning ICML 2025
Variance-Dependent Regret Bounds for Nonstationary Linear Bandits AISTATS 2025
Risk Bounds of Accelerated SGD for Overparameterized Linear Regression ICLR 2024
Uncertainty-Aware Reward-Free Exploration with General Function Approximation ICML 2024
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path ICML 2023
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency COLT 2023
Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits ICML 2023
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes ICML 2023
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs AISTATS 2022
Learning Neural Contextual Bandits through Perturbed Rewards ICLR 2022
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima ALT 2022
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation AISTATS 2022
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs NIPS 2022
Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization ICML 2022
Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games ALT 2022
Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium NIPS 2022
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions NIPS 2022
Neural Thompson Sampling ICLR 2021
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation NIPS 2021
Variance-Aware Off-Policy Evaluation with Linear Function Approximation NIPS 2021
Pure Exploration in Kernel and Neural Bandits NIPS 2021
Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints NIPS 2021
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation NIPS 2021
Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs NIPS 2021
Iterative Teacher-Aware Learning NIPS 2021
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes COLT 2021
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation ICML 2021
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping ICML 2021
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks AAAI 2020
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks IJCAI 2020
Stochastic Nested Variance Reduction for Nonconvex Optimization JMLR 2020
Stochastic Recursive Variance-Reduced Cubic Regularization Methods AISTATS 2020
Neural Contextual Bandits with UCB-based Exploration ICML 2020
Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization AISTATS 2020
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization ICML 2019
Stochastic Variance-Reduced Cubic Regularization Methods JMLR 2019
Stochastic Variance-Reduced Cubic Regularized Newton Methods ICML 2018
Stochastic Nested Variance Reduction for Nonconvex Optimization NIPS 2018