Jason D. Lee
Jason D. Lee
Assistant Professor of Electrical Engineering, Princeton University
Verified email at princeton.edu - Homepage
Cited by
Cited by
Gradient descent only converges to minimizers
JD Lee, M Simchowitz, MI Jordan, B Recht
Conference on learning theory, 1246-1257, 2016
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
Matrix completion has no spurious local minimum
R Ge, JD Lee, T Ma
Advances in Neural Information Processing Systems, 2973-2981, 2016
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
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
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
Proximal Newton-type methods for minimizing composite functions
JD Lee, Y Sun, MA Saunders
SIAM Journal on Optimization 24 (3), 1420-1443, 2014
A kernelized Stein discrepancy for goodness-of-fit tests
Q Liu, J Lee, M Jordan
International conference on machine learning, 276-284, 2016
Practical large-scale optimization for max-norm regularization
JD Lee, B Recht, N Srebro, J Tropp, RR Salakhutdinov
Advances in neural information processing systems, 1297-1305, 2010
Learning one-hidden-layer neural networks with landscape design
R Ge, JD Lee, T Ma
arXiv preprint arXiv:1711.00501, 2017
Learning the structure of mixed graphical models
JD Lee, TJ Hastie
Journal of Computational and Graphical Statistics 24 (1), 230-253, 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
On the power of over-parametrization in neural networks with quadratic activation
SS Du, JD Lee
arXiv preprint arXiv:1803.01206, 2018
Implicit bias of gradient descent on linear convolutional networks
S Gunasekar, JD Lee, D Soudry, N Srebro
Advances in Neural Information Processing Systems, 9461-9471, 2018
Communication-efficient distributed statistical inference
MI Jordan, JD Lee, Y Yang
Journal of the American Statistical Association, 2018
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, 1067-1077, 2017
Characterizing implicit bias in terms of optimization geometry
S Gunasekar, J Lee, D Soudry, N Srebro
arXiv preprint arXiv:1802.08246, 2018
Proximal Newton-type methods for convex optimization
JD Lee, Y Sun, M Saunders
Advances in Neural Information Processing Systems, 827-835, 2012
First-order methods almost always avoid saddle points
JD Lee, I Panageas, G Piliouras, M Simchowitz, MI Jordan, B Recht
arXiv preprint arXiv:1710.07406, 2017
Deterministic matrices matching the compressed sensing phase transitions of Gaussian random matrices
H Monajemi, S Jafarpour, M Gavish, DL Donoho, ...
Proceedings of the National Academy of Sciences 110 (4), 1181-1186, 2013
The system can't perform the operation now. Try again later.
Articles 1–20