Dongruo Zhou

16

Papers

12

Total Citations

Papers (16)

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

Federated In-Context Learning: Iterative Refinement for Improved Answer Quality

Uncertainty-Aware Reward-Free Exploration with General Function Approximation

Stochastic Nested Variance Reduction for Nonconvex Optimization

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

Variance-Aware Off-Policy Evaluation with Linear Function Approximation

Pure Exploration in Kernel and Neural Bandits

Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints

Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation

Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs

Iterative Teacher-Aware Learning

NeurIPS 2021arXiv

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

Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions

Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs

Stochastic Variance-Reduced Cubic Regularized Newton Methods

Lower Bounds for Smooth Nonconvex Finite-Sum Optimization