Talks

Recent Progresses on the Theory of Multi-Agent Reinforcement Learning and Games.
Guest Lecture at CS332, Stanford University, Oct 2022.

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.

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.
Facebook AI Research, March 2020.

ProxQuant: Quantized Neural Networks via Proximal Operators
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)
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.