1. Yichun Hu, Nathan Kallus, Xiaojie Mao (2022) Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes.
    • Operations Research 70(6):3261-3281.
    • Preliminary version in 33rd Conference on Learning Theory (COLT), 2020.
    • Finalist, INFORMS Applied Probability Society Best Student Paper Competition, 2020.
    • Recorded talk: COLT [15-min video]
  2. Yichun Hu, Nathan Kallus, Xiaojie Mao (2022) Fast Rates for Contextual Linear Optimization.
    • Management Science 68(6):4236-4245.
  3. Mia Garrard, Hanson Wang, Benjamin Letham, Zehui Wang, Yin Huang, Yichun Hu, Chad Zhou, Norm Zhou, Eytan Bakshy (2021) Practical Policy Optimization with Personalized Experimentation.
    • NeurIPS Workshop on Causal Inference Challenges in Sequential Decision Making: Bridging Theory and Practice.

Under Revision

  1. Yichun Hu, Nathan Kallus, Masatoshi Uehara. Fast Rates for the Regret of Offline Reinforcement Learning.
    • Minor Revision at Mathematics of Operations Research.
    • Preliminary version in 34th Conference on Learning Theory (COLT), 2021.
    • Recorded talks: RL Theory Seminar [60-min video], COLT [18-min video]
  2. Yichun Hu, Nathan Kallus. DTR Bandit: Learning to Make Response-Adaptive Decisions with Low Regret.
    • Moderate Revision at Journal of the American Statistical Association.