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Jason D. Lee
Jason D. Lee
Associate Professor of Electrical Engineering and Computer Science, Princeton University
Подтвержден адрес электронной почты в домене princeton.edu - Главная страница
Название
Процитировано
Процитировано
Год
Gradient descent finds global minima of deep neural networks
SS Du, JD Lee, H Li, L Wang, X Zhai
arXiv preprint arXiv:1811.03804, 2018
11392018
Exact post-selection inference, with application to the lasso
JD Lee, DL Sun, Y Sun, JE Taylor
The Annals of Statistics 44 (3), 907-927, 2016
8792016
Gradient descent only converges to minimizers
JD Lee, M Simchowitz, MI Jordan, B Recht
Conference on learning theory, 1246-1257, 2016
818*2016
Matrix completion has no spurious local minimum
R Ge, JD Lee, T Ma
Advances in neural information processing systems 29, 2016
6812016
On the theory of policy gradient methods: Optimality, approximation, and distribution shift
A Agarwal, SM Kakade, JD Lee, G Mahajan
Journal of Machine Learning Research 22 (98), 1-76, 2021
586*2021
Matrix completion and low-rank SVD via fast alternating least squares
T Hastie, R Mazumder, J Lee, R Zadeh
Journal of Machine Learning Research, 2014
5562014
A kernelized Stein discrepancy for goodness-of-fit tests
Q Liu, J Lee, M Jordan
International conference on machine learning, 276-284, 2016
4632016
Theoretical insights into the optimization landscape of over-parameterized shallow neural networks
M Soltanolkotabi, A Javanmard, JD Lee
IEEE Transactions on Information Theory 65 (2), 742-769, 2018
4352018
Proximal Newton-type methods for minimizing composite functions
JD Lee, Y Sun, MA Saunders
SIAM Journal on Optimization 24 (3), 1420-1443, 2014
424*2014
Communication-efficient distributed statistical inference
MI Jordan, JD Lee, Y Yang
Journal of the American Statistical Association, 2018
3862018
Implicit bias of gradient descent on linear convolutional networks
S Gunasekar, JD Lee, D Soudry, N Srebro
Advances in neural information processing systems 31, 2018
3812018
Characterizing implicit bias in terms of optimization geometry
S Gunasekar, J Lee, D Soudry, N Srebro
International Conference on Machine Learning, 1832-1841, 2018
3742018
Solving a class of non-convex min-max games using iterative first order methods
M Nouiehed, M Sanjabi, T Huang, JD Lee, M Razaviyayn
Advances in Neural Information Processing Systems 32, 2019
321*2019
First-order methods almost always avoid strict saddle points
JD Lee, I Panageas, G Piliouras, M Simchowitz, MI Jordan, B Recht
Mathematical programming 176 (1-2), 311-337, 2019
3182019
Learning one-hidden-layer neural networks with landscape design
R Ge, JD Lee, T Ma
arXiv preprint arXiv:1711.00501, 2017
2842017
Kernel and rich regimes in overparametrized models
B Woodworth, S Gunasekar, JD Lee, E Moroshko, P Savarese, I Golan, ...
Conference on Learning Theory, 3635-3673, 2020
2802020
Gradient descent can take exponential time to escape saddle points
SS Du, C Jin, JD Lee, MI Jordan, A Singh, B Poczos
Advances in neural information processing systems 30, 2017
2642017
On the power of over-parametrization in neural networks with quadratic activation
S Du, J Lee
International conference on machine learning, 1329-1338, 2018
2592018
Learning the structure of mixed graphical models
JD Lee, TJ Hastie
Journal of Computational and Graphical Statistics 24 (1), 230-253, 2015
233*2015
Gradient descent learns one-hidden-layer cnn: Don’t be afraid of spurious local minima
S Du, J Lee, Y Tian, A Singh, B Poczos
International Conference on Machine Learning, 1339-1348, 2018
2302018
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