Yichun Hu 胡逸纯

Yichun Hu 胡逸纯

Ph.D. Candidate

Cornell University



I am a Ph.D. candidate in Operations Research at Cornell University, advised by Professor Nathan Kallus at Cornell Tech. I am broadly interested in various data-driven decision making problems, especially in sequential settings. I obtained my B.S. in Mathematics and Applied Mathematics and B.A. in Economics from Peking University.

  • Data-driven Decision Making
  • Contextual Bandits
  • Stochastic Optimization
  • PhD in Operations Research, 2023 (Expected)

    Cornell University

  • B.S. in Mathematics and Applied Mathematics, B.A. in Economics, 2017

    Peking University

Research Papers


  1. Yichun Hu, Nathan Kallus, Xiaojie Mao. Fast Rates for Contextual Linear Optimization. Management Science, accepted.
  2. Yichun Hu, Nathan Kallus, Xiaojie Mao. Smooth Contextual Bandits: Bridging the Parametric and Non-differentiableRegret Regimes. Operations Research (Article in Advance), 2022.
    • Preliminary version in 33rd Conference on Learning Theory (COLT), 2020.
    • Finalist, INFORMS Applied Probability Society Best Student Paper Competition, 2020.
  3. Yichun Hu, Nathan Kallus, Masatoshi Uehara. Fast Rates for the Regret of Offline Reinforcement Learning. 34th Conferenceon Learning Theory (COLT), 2021.
    • Journal version under review.

Under Review/Revision

  1. Yichun Hu, Nathan Kallus. DTR Bandit: Learning to Make Response-Adaptive Decisions with Low Regret. Major Revision at Journal of the American Statistical Association (JASA).

Workshop Papers

  1. Mia Garrard, Hanson Wang, Benjamin Letham, Shaun Singh, Abbas Kazerouni, Sarah Tan, Zehui Wang, Yin Huang, Yichun Hu, Chad Zhou, Norm Zhou, Eytan Bakshy. Practical Policy Optimization with Personalized Experimentation. NeurIPS Workshop on Causal Inference Challenges in Sequential Decision Making: Bridging Theory and Practice, 2021.


  • INFORMS Optimization Society Conference, Greenville, SC, 03/2022
  • RL Theory Seminar, Virtual, 11/2021 [video]
  • INFORMS Annual Meeting, Anaheim, CA, 10/2021
  • 16th INFORMS Workshop on Data Mining and Decision Analytics, Anaheim, CA, 10/2021
  • 34th Annual Conference on Learning Theory (COLT 2021), Boulder, CO, 08/2021 [video]
  • INFORMS Annual Meeting, Virtual, 11/2020
  • 15th INFORMS Workshop on Data Mining and Decision Analytics, Virtual, 11/2020
  • 33rd Annual Conference on Learning Theory (COLT 2020), Virtual, 07/2020 [video]
  • INFORMS Annual Meeting, Seattle, WA, 10/2019
  • 14th INFORMS Workshop on Data Mining and Decision Analytics, Seattle, WA, 10/2019
  • Cornell ORIE Young Researchers Workshop, Ithaca, NY, 10/2019