Wei Hu

Wei Hu 

Wei Hu (胡威)
Ph.D. Student
Department of Computer Science
Princeton University

Email: huwei [at] cs [dot] princeton [dot] edu

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I am a third year PhD student in the Department of Computer Science at Princeton University, affiliated with the Theory Group, the Theoretical Machine Learning Group, and Sanjeev Arora’s Group. I am very fortunate to be advised by Sanjeev Arora. Previously, I did my undergrad at Tsinghua University, where I was a member of Yao Class.

My current research interests are in the intersection of theoretical computer science and machine learning, with the goal of achieving better theoretical understanding of machine learning models and algorithms, as well as designing practical and provably correct algorithms for machine learning and optimization problems.


(Authors are ordered alphabetically, unless otherwise noted)


On the Dynamics of Gradient Descent for Training Deep Neural Networks

Princeton-IAS Theoretical Machine Learning Seminar, October 2018, Princeton, NJ, USA

Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced

ICML 2018 Workshop on Modern Trends in Nonconvex Optimization for Machine Learning, July 2018, Stockholm, Sweden

An Analysis of the t-SNE Algorithm for Data Visualization

COLT, July 2018, Stockholm, Sweden

Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls

NIPS, December 2017, Long Beach, CA, USA

New Characterizations in Turnstile Streams with Applications

CCC, May 2016, Tokyo, Japan

Combinatorial Multi-Armed Bandit with General Reward Functions

Microsoft Research Asia Theory Group Seminar, March 2016, Beijing, China


Princeton University


Selected Awards