I am a third year PhD student in computer science at University of California, Berkeley, advised by Michael I. Jordan. I am also fortunate to work with Benjamin Recht. I am affiliated with Berkeley AI Research (BAIR) and RISE Lab.
I am broadly interested in theoretical and empirical foundations of machine learning, with a focus on designing machine learning algorithms that are equitable and reliable. Other interests include game theory and mechanism design.
I obtained my Bachelor of Science triple majored in physics, computer science and mathematics from Hong Kong University of Science and Technology in 2018. I am very grateful for the mentorship of Kwok Yip Szeto, Dit-Yan Yeung and Mordecai J. Golin who guided me to research on statistical physics, machine learning and graph theory.
wsguo at berkeley dot edu
Office: 523 Soda Hall, Berkeley, CA 94709.
(asterisk indicates joint or alphabetical authorship)
Do Offline Metrics Predict Online Performance in Recommender Systems? [PDF]
Karl Krauth, Sarah Dean*, Alex Zhao*, Wenshuo Guo*, Mihaela Curmei*, Benjamin Recht, Michael I. Jordan. 2020. (In submission)
Robust Optimization for Fairness with Noisy Protected Groups [PDF]
Serena Wang*, Wenshuo Guo*, Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, Michael I. Jordan. (In submission)
Also appears at Workshop on Mechanism Design for Social Good (MD4SG), 2020
Heavily-Constrained Learning with Lagrange Multiplier Models [PDF]
Harikrishna Narasimhan, Andrew Cotter, Yichen Zhou, Serena Wang, Wenshuo Guo. 2020.
Conference on Neural Information Processing Systems (NeurIPS), 2020.
Finding Equilibrium in Multi-Agent Games with Payoff Uncertainty [PDF | slides | video]
Wenshuo Guo, Mihaela Curmei, Serena Wang, Benjamin Recht, Michael I. Jordan.
International Conference on Machine Learning (ICML),
Workshop on Theoretical Foundations of Reinforcement Learning, 2020
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.
Neural Kernel Without Tangents [PDF | slides | video]
Vaishaal Shankar, Alex Fang, Wenshuo Guo, Sara Fridovich-Keil, Ludwig Schmidt, Jonathan Ragan-Kelly, Benjamin Recht.
International Conference on Machine Learning (ICML), 2020.
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.
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.
I have participated in teaching two courses at UC Berkeley. CS 281 is a graduate-level class focuses on the foundations of statistical learning theory; Data Science 102 is a senior level undergraduate class for the data science major, focuses on the desicion side of machine learning from large-scale data.
Data 102: Data, Inference, and Decisions
Jacob Steinhardt, Moritz Hardt, 2020 Spring
CS 281: Statistical Learning Theory
Benjamin Recht, Moritz Hardt, 2019 Fall
I serve as the social chair for WICSE at Berkeley from 2020-2021. I am part of the Berkeley AI Research (BAIR) undergraduate mentor program starting from 2019. In Fall 2020, I am fortunate to organize a weekly reading group on altruism with the Steinhardt group.
Reviewer for: ICML, ICLR
In my spare time I enjoy overworking : ) While not working I like running, hiking, music, reading books. I had a great time participating in the Berkeley Half Marathon, and being a mezzo-soprano at UC Berkeley University Choir. I also enjoy practicing my piano skills, writing classical Chinese poems from time to time, and being an amateur photographer mostly taking snaps of the lovely nature.
I am happy to chat about grad school and research. Feel free to drop me an email.