Haipeng Luo

Haipeng Luo 

IBM Early Career Chair and Associate Professor
Thomas Lord Department of Computer Science
University of Southern California

Office: SAL 216
Email: haipengl at usc dot edu

About me

I am an associate professor in the Thomas Lord Department of Computer Science at the University of Southern California. Previously I spent one year at Microsoft Research, NYC as a postdoctoral researcher. I obtained my PhD from Princeton University, where I was fortunate enough to be advised by Rob Schapire and also to work closely with Elad Hazan. I received my bachelor degree at Peking University working with Professor Zhen Xiao.

Research interests

My research interest is in developing practical machine learning algorithms with strong theoretical guarantees, with a focus on

  • online learning

  • bandit problems

  • reinforcement learning

  • learning in games

  • fast and scalable optimization methods

See below for some of my representative papers (a full list can be found here or at Google Scholar).

Representative Papers

  • [COLT 2021 Best Paper Award] Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach.
    Chen-Yu Wei and Haipeng Luo.

  • [NeurIPS 2021 Oral] The Best of Both Worlds: Stochastic and Adversarial Episodic MDPs with Unknown Transition.
    Tiancheng Jin, Longbo Huang, and Haipeng Luo.

  • [NeurIPS 2020 Oral] Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs.
    Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei, and Mengxiao Zhang.

  • [COLT 2018 Best Student Paper Award] Logistic Regression: The Importance of Being Improper.
    Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, and Karthik Sridharan.

  • [COLT 2018] More Adaptive Algorithms for Adversarial Bandits.
    Chen-Yu Wei and Haipeng Luo.

  • [COLT 2017] Corralling a Band of Bandit Algorithms.
    Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, and Robert E. Schapire.

  • [NeurIPS 2015 Best Paper Award] Fast Convergence of Regularized Learning in Games.
    Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, and Robert E. Schapire.

  • [ICML 2015 Best Paper Award] Optimal and Adaptive Algorithms for Online Boosting.
    Alina Beygelzimer, Satyen Kale, and Haipeng Luo.

  • [COLT 2015] Achieving All with No Parameters: AdaNormalHedge.
    Haipeng Luo and Robert E. Schapire.