[Google Scholar | GitHub ]

  1. Robust Learning of Optimal Auctions [PDF]
    Wenshuo Guo, Michael I. Jordan, Emmanouil Zampetakis. 2021. (α-β order). (In submission)

  2. Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits [PDF | code]
    Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, Vidya Muthukumar, Ashwin Pananjady. 2021. (In submission)
    Short version to appear at ICML'21 Workshop on Human-AI Collaboration in Sequential Decision-Making (spotlight talk) and ICML'21 Workshop on Reinforcement Learning Theory.

  3. Online Learning of Competitive Equilibria in Exchange Economies [PDF]
    Wenshuo Guo, Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica. 2021. (In submission)

  4. The Stereotyping Problem in Collaboratively Filtered Recommender Systems [PDF]
    Wenshuo Guo*, Karl Krauth*, Michael I. Jordan, Nikhil Garg. 2021. (In submission)

  5. Test-time Collective Prediction [PDF]
    Celestine Mendler-Dünner, Wenshuo Guo, Stephen Bates, Michael I. Jordan. 2021. (In submission)

  6. Multi-Source Causal Inference Using Control Variates [PDF]
    Wenshuo Guo*, Serena Wang*, Peng Ding, Yixin Wang, Michael I. Jordan. 2021. *equal contribution. (In submission)

  7. A Variational Inequality Approach to Bayesian Regression Games [PDF]
    Wenshuo Guo, Michael I. Jordan, Tianyi Lin. 2021. (α-β order).
    IEEE Conference on Decision and Control (CDC), 2021.

  8. Do Offline Metrics Predict Online Performance in Recommender Systems? [PDF | Berkeley RecLab]
    Karl Krauth, Sarah Dean*, Alex Zhao*, Wenshuo Guo*, Mihaela Curmei*, Benjamin Recht, Michael I. Jordan. 2020. *equal contribution.
    Short version at the Workshop on Consequential Decisions in Dynamic Environments, NeurIPS 2020.

  9. Finding Equilibrium in Multi-Agent Games with Payoff Uncertainty [PDF | slides | video]
    Wenshuo Guo, Mihaela Curmei, Serena Wang, Benjamin Recht, Michael I. Jordan.
    Short version at the Workshop on Theoretical Foundations of Reinforcement Learning, ICML 2020.

  10. Robust Optimization for Fairness with Noisy Protected Groups [PDF | code]
    Serena Wang*, Wenshuo Guo*, Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, Michael I. Jordan. *equal contribution.
    Conference on Neural Information Processing Systems (NeurIPS), 2020.
    Short version at Workshop on Mechanism Design for Social Good (MD4SG), 2020

  11. Approximate Heavily-Constrained Learning with Lagrange Multiplier Models [PDF | code]
    Harikrishna Narasimhan, Andrew Cotter, Yichen Zhou, Serena Wang, Wenshuo Guo. 2020.
    Conference on Neural Information Processing Systems (NeurIPS), 2020.

  12. Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter [PDF | slides]
    Wenshuo Guo, Nhat Ho, Michael I. Jordan.
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.

  13. Neural Kernel Without Tangents [PDF | slides | video | code ]
    Vaishaal Shankar, Alex Fang, Wenshuo Guo, Sara Fridovich-Keil, Ludwig Schmidt, Jonathan Ragan-Kelly, Benjamin Recht.
    International Conference on Machine Learning (ICML), 2020.

  14. Spin Model of Two Random Walkers in Complex Networks [PDF]
    Wenshuo Guo, Juntao Wang, Szeto Kwok Yip.
    International Conference on Complex Networks and Their Applications (CNA), 2017.

  15. Optimization of Financial Network Stability by Genetic Algorithm [PDF]
    Juntao Wang, Wenshuo Guo, Szeto Kwok Yip.
    IEEE/WIC/ACM International Conference on Web Intelligence (WI), 2017.

  16. Minimization of Systemic Risk for Directed Network Using Genetic Algorithm [PDF]
    Wenshuo Guo, Szeto Kwok Yip.
    International Conference on the Applications of Evolutionary Computation (Evo*), 2017.