Yu Bai

Yu Bai 

About me: I am a Senior Research Scientist at Salesforce AI Research in Palo Alto, CA. Before joining Salesforce, I completed my PhD in Statistics at Stanford University (specializing in machine learning) in September 2019, where I was fortunate to be advised by Prof. John Duchi and was a member of the Machine Learning Group. During my PhD I also spent times at the research labs of Google and Amazon. Prior to Stanford, I was an undergrad in mathematics at Peking University.

My research interest lies broadly in machine learning, with recent focus on

  • Theoretical foundations of deep learning (blog post);

  • Reinforcement learning theory (slides on partially observable RL);

  • Multi-agent reinforcement learning and games (blog post, slides on MARL, slides on Extensive-Form Games);

  • Uncertainty quantification (slides).


  • [Mar 2023] I will serve as an Area Chair for NeurIPS 2023.

  • [Jan 2023] Three papers accepted at ICLR 2023.

  • [Nov 2022] Excited to be giving an invited talk “Recent Progresses on the Theory of Multi-Agent Reinforcement Learning and Games” at Stanford CS332.

  • [Sep 2022] Four papers accepted at NeurIPS 2022.

  • [May 2022] Excited to be speaking at the RL theory seminar about our work on sample-efficient learning of general-sum Markov Games with a large number of players!

  • [May 2022] Our paper on near-optimal learning in extensive-form games is accepted at ICML 2022. We achieve this by two new algorithms (Balanced OMD, Balanced CFR).


yu.bai (at) salesforce.com

Curriculum Vitae | Google Scholar Profile | Github




Other technical reports


  • When Can We Learn General-Sum Markov Games Sample-Efficiently with A Large Number of Players?
    RL Theory Virtual Seminars, May 2022.

  • Near-Optimal Learning of Extensive-Form Games with Imperfect Information.
    Learning and Games Program, Simons Institute, April 2022.
    CISS Conference, Princeton University, March 2022.

  • Understanding the Under-Coverage Bias in Uncertainty Estimation.
    Statistics Department Seminar, Rutgers University, October 2021.
    Spotlight presentation at ICML 2021 Workshop on Distribution-free Uncertainty Quantification, July 2021.

  • Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games.
    Spotlight presentation at ICML 2021 Workshop on Reinforcement Learning Theory, July 2021.

  • How Important is the Train-Validtaion Split in Meta-Learning?
    One World Seminar on the Mathematics of Machine Learning, October 2020.

  • Provable Self-Play Algorithms for Competitive Reinforcement Learning.
    ICML, July 2020.
    Facebook AI Research, March 2020.

  • Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks.
    Simons Institute, August 2020.
    ICLR, April 2020.

  • Subgradient Descent Learns Orthogonal Dictionaries.
    ICLR, May 2019, New Orleans, LA.

  • ProxQuant: Quantized Neural Networks via Proximal Operators
    ICLR, May 2019, New Orleans, LA.
    Bytedance AI Lab, Dec 2018, Menlo Park, CA.
    Amazon AI, Sep 2018, East Palo Alto, CA.

  • On the Generalization and Approximation in Generative Adversarial Networks (GANs)
    ICLR, May 2019, New Orleans, LA.
    Google Brain, Nov 2018, Mountain View, CA.
    Salesforce Research, Nov 2018, Palo Alto, CA.
    Stanford ML Seminar, Oct 2018, Stanford, CA.

  • Optimization Landscape of some Non-convex Learning Problems
    Stanford Theory Seminar, Apr 2018, Stanford, CA.
    Stanford ML Seminar, Apr 2017, Stanford, CA.


  • Area chair / Senior program committee: NeurIPS (2023), AIStats (2023).

  • Conference reviewing: NeurIPS (2018-2022), ICLR (2019-2023), ICML (2019-2021, 2023), COLT (2019-2020, 2022-2023), FOCS (2022), AIStats (2020), IEEE-ISIT (2018).

  • Journal reviewing: TMLR (Transactions of Machine Learning Research), The Annals of Statistics, JASA (Journal of the American Statistical Association), JRSS-B (Journal of the Royal Statistical Society: Series B), JMLR (Journal of Machine Learning Research), IEEE-TSP (Transactions on Signal Processing), SICON (SIAM Journal on Control and Optimization).