Heyang Zhao

Breaking the Total Variance Barrier: Sharp Sample Complexity for Linear Heteroscedastic Bandits with Fixed Action Set

Towards a Sharp Analysis of Offline Policy Learning for $f$-Divergence-Regularized Contextual Bandits

Best-of-Majority: Minimax-Optimal Strategy for Pass@k Inference Scaling

Logarithmic Regret for Online KL-Regularized Reinforcement Learning

Sharp Analysis for KL-Regularized Contextual Bandits and RLHF

Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration

Sharp Analysis for KL-Regularized Contextual Bandits and RLHF