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Dongruo Zhou

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

Achievements

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+12 more ↓

🐣 Hot Topic Early Bird 🌍 Conference Polyglot (11) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🏃 Academic Marathon (7)

🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) 🗺️ Taxonomy Completionist (35) 🤝 Dynamic Duo (36) 👑 Triple Crown 🏆 Grand Slam 🔬 Deep Specialist (13) 🏆 Keyword Champion (2) 🗃️ Keyword Collector (125) ⚡ Prolific Year (6) 🔥 Unstoppable (8) 💎 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