E-mail: bhzhang at cs dot cmu dot edu
Office: GHC 9231
CV, Google Scholar

I am a fifth-year PhD student in the Computer Science Department at Carnegie Mellon University, where I am fortunate to be advised by Prof. Tuomas Sandholm. Prior to CMU, I completed my undergraduate and master’s degrees at Stanford University, where I worked with Prof. Greg Valiant.

My current research interests lie in computational game theory, especially equilibrium finding in extensive-form games. I have also done work in adversarial robustness, fairness in machine learning, and quantum computing.

Publications

“with” denotes alphabetical ordering of authors. * denotes equal contribution.

  2. Efficient Φ-Regret Minimization with Low-Degree Swap Deviations in Extensive-Form Games
    Brian Hu Zhang, Ioannis Anagnostides, Gabriele Farina, Tuomas Sandholm
    arXiv 2024

  3. Mediator Interpretation and Faster Learning Algorithms for Linear Correlated Equilibria in General Extensive-Form Games
    Brian Hu Zhang, Gabriele Farina, Tuomas Sandholm
    ICLR 2024

  4. On the Outcome Equivalence of Extensive-Form and Behavioral Correlated Equilibria
    Brian Hu Zhang, Tuomas Sandholm
    AAAI 2024

  5. Hidden-Role Games: Equilibrium Concepts and Computation
    Luca Carminati*, Brian Hu Zhang*, Gabriele Farina, Nicola Gatti, Tuomas Sandholm
    arXiv 2023

  6. Steering No-Regret Learners to Optimal Equilibria
    Brian Hu Zhang*, Gabriele Farina*, Ioannis Anagnostides, Federico Cacciamani, Stephen McAleer, Andreas Haupt, Andrea Celli, Nicola Gatti, Vincent Conitzer, Tuomas Sandholm
    arXiv 2023

  7. Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games
    Brian Hu Zhang*, Gabriele Farina*, Ioannis Anagnostides, Federico Cacciamani, Stephen McAleer, Andreas Haupt, Andrea Celli, Nicola Gatti, Vincent Conitzer, Tuomas Sandholm
    NeurIPS 2023

  8. Subgame Solving in Adversarial Team Games
    Brian Hu Zhang*, Luca Carminati*, Federico Cacciamani, Gabriele Farina, Pierriccardo Olivieri, Nicola Gatti, Tuomas Sandholm
    NeurIPS 2022

  9. Polynomial-Time Optimal Equilibria with a Mediator in Extensive-Form Games
    Brian Hu Zhang, Tuomas Sandholm
    NeurIPS 2022

  10. Optimal Correlated Equilibria in General-Sum Extensive-Form Games: Fixed-Parameter Algorithms, Hardness, and Two-Sided Column-Generation
    Brian Hu Zhang, Gabriele Farina, Andrea Celli, Tuomas Sandholm
    EC 2022

  11. Team Belief DAG: Generalizing the Sequence Form to Team Games for Fast Computation of Correlated Team Max-Min Equilibria via Regret Minimization
    Brian Hu Zhang, Gabriele Farina, Tuomas Sandholm
    ICML 2023; arXiv 2022

  12. Polynomial-Time Sum-of-Squares Can Robustly Estimate Mean and Covariance of Gaussians Optimally
    with Pravesh K. Kothari, Peter Manohar
    ALT 2022

  13. Team Correlated Equilibria in Zero-Sum Extensive-Form Games via Tree Decompositions
    Brian Hu Zhang, Tuomas Sandholm
    AAAI 2022

  14. Subgame solving without common knowledge
    Brian Hu Zhang, Tuomas Sandholm
    NeurIPS 2021 Spotlight and AAAI Workshop on Reinforcement Learning in Games 2022 Oral Presentation

  15. Finding and Certifying (Near-)Optimal Strategies in Black-Box Extensive-Form Games
    Brian Hu Zhang, Tuomas Sandholm
    AAAI 2021 and AAAI Workshop on Reinforcement Learning in Games 2021 Oral Presentation

  16. Small Nash Equilibrium Certificates in Very Large Games
    Brian Hu Zhang, Tuomas Sandholm
    NeurIPS 2020

  17. Sparsified Linear Programming for Zero-Sum Equilibrium Finding
    Brian Hu Zhang, Tuomas Sandholm
    ICML 2020

  18. A Spectral View of Adversarially Robust Features
    Shivam Garg, Vatsal Sharan*, Brian Hu Zhang*, Gregory Valiant
    NeurIPS 2018 Spotlight

  19. Mitigating Unwanted Biases with Adversarial Learning
    Brian Hu Zhang, Blake Lemoine, Margaret Mitchell
    AIES 2018

  20. Advantages of Unfair Quantum Ground-State Sampling
    Brian Hu Zhang, Gene Wagenbreth, Victor Martin-Mayor, Itay Hen
    Scientific Reports 2016

Teaching and Service

Service

Teaching Assistanceships

Carnegie Mellon University

Stanford University