Jason D. Lee |
About Me
I am an assistant professor of Electrical Engineering and Computer Science (secondary) in Princeton University and a member of the Theoretical Machine Learning Group. Previously, I was a member of the IAS and an assistant professor at USC for three years. Before that, I was a postdoc in the Computer Science Department at UC Berkeley working with Michael I. Jordan, and also collaborated with Ben Recht. I received my PhD in Applied Math advised by Trevor Hastie and Jonathan Taylor. I received a BS in Mathematics from Duke University advised by Mauro Maggioni.I am a native of Cupertino, CA.
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My research interests are broadly in
Kankakee county circuit clerk. Foundations of Deep Learning (slides)(video)
Representation Learning (slides)(video)
Foundations of Deep Reinforcement Learning (slides)(video 1)(video 2)
Princeton PhD students interested in machine learning, statistics, or optimization research, please contact me; I advise students in Computer Science, Electrical Engineering, Math, ORFE, and PACM. I am recruiting PhD students and postdoctoral scholars starting in 2021 at Princeton University, please email me a CV apply.
My current focus is on machine learning with a focus on foundations of deep learning, reprsentation learning, and deep reinforcement learning. See my talk at MIT slides and video or my tutorial at the Simons Institute: tutorial slides and video.
I am also happy to host remote visitors. Summer visitors please contact me around February to schedule your visit. See a list of past visitors at here.
Awards
Sloan Research Fellow in Computer Science, Alfred P. Sloan Foundation
Finalist for Best Paper Prize for Young Researchers in Continuous Optimization
ICML 2018 Workshop on Nonconvex Optimization for MLBest Paper Award for ‘‘Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced'
NIPS 2016 Best Student Paper Award for ‘‘Matrix Completion has no Spurious Local Minima'.
Selected Publications
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot.
Jingtong Su, Yihang Chen, Tianle Cai, Tianhao Wu, Ruiqi Gao, Liwei Wang, and Jason D. Lee.
NeurIPS 2020.
Predicting What You Already Know Helps: Provable Self-Supervised Learning.
Jason D. Lee, Qi Lei, Nikunj Saunshi, and Jiacheng Zhuo.
Jason Lee Hollywood Unlocked
Towards Understanding Hierarchical Learning: Benefits of Neural Representations.
Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, and Richard Socher.
NeurIPS 2020.
Jason Lee Home In Denton Tx
Few-Shot Learning via Learning the Representation, Provably.
Simon S. Du, Wei Hu, Sham M. Kakade, Jason D. Lee, and Qi Lei.
Shape Matters: Understanding the Implicit Bias of the Noise Covariance.
Jeff Z. HaoChen, Colin Wei, Jason D. Lee, and Tengyu Ma.
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks .
Yu Bai and Jason D. Lee.
ICLR 2020.
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift
Alekh Agarwal, Sham M. Kakade, Jason D. Lee, and Gaurav Mahajan.
JMLR.
Jason Lee Hughston Homes
Gradient Descent Finds Global Minima of Deep Neural Networks
Simon S. Du, Jason D. Lee, Haochuan Li, Liwei Wang, and Xiyu Zhai.
ICML 2019.
Jason London
Gradient Descent Converges to Minimizers.
Jason D. Lee, Max Simchowitz, Michael I. Jordan, and Benjamin Recht.
COLT 2016
Matrix Completion has No Spurious Local Minimum.
Rong Ge, Jason D. Lee, and Tengyu Ma.
Best Student Paper Award at NeurIPS 2016.
Exact Post-Selection Inference with the Lasso.
Jason D. Lee, Dennis L Sun, Yuekai Sun, and Jonathan Taylor.
Annals of Statistics 2016.