Yu Bai
About MeI am a Senior Research Scientist at Salesforce AI Research in Palo Alto, CA. My research interest lies broadly in machine learning, such as deep learning, reinforcement learning, learning in games, and uncertainty quantification. 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
News:
Recent Work
Research Focus and Selected PublicationsMulti-Agent Reinforcement Learning Theory
We developed the first line of provably efficient algorithms for multi-agent reinforcement learning.
Deep Learning Theory
We developed optimization and generalization results for overparametrized neural networks beyond the Neural Tangent Kenrels (NTK) regime, and identified provable advantages over the NTK regime.
Partially Observable Reinforcement Learning
We designed sharp sample-efficient algorithms and studied the fundamental limits for partially observable reinforcement learning.
Learning in Games
We designed near-optimal algorithms for learning equilibria in various multi-player games under bandit feedback.
Uncertainty Quantification in Machine Learning
We gave precise theoretical characterizations of the calibration and coverage of vanilla machine learning algorithms, and developed new uncertainty quantificaiton algorithms with valid guarantees and improved efficiency.
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