Jason D. Lee
Jason D. Lee
Assistant Professor of Data Sciences and Operations, University of Southern California
確認したメール アドレス: marshall.usc.edu - ホームページ
タイトル引用先
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
2612016
Matrix completion has no spurious local minimum
R Ge, JD Lee, T Ma
Advances in Neural Information Processing Systems, 2973-2981, 2016
2452016
Gradient descent only converges to minimizers
JD Lee, M Simchowitz, MI Jordan, B Recht
Conference on learning theory, 1246-1257, 2016
1752016
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
1412014
Proximal Newton-type methods for minimizing composite functions
JD Lee, Y Sun, MA Saunders
SIAM Journal on Optimization 24 (3), 1420-1443, 2014
1412014
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
1282010
Gradient descent converges to minimizers
JD Lee, M Simchowitz, MI Jordan, B Recht
arXiv preprint arXiv:1602.04915, 2016
1042016
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, 2019
942019
Learning the structure of mixed graphical models
JD Lee, TJ Hastie
Journal of Computational and Graphical Statistics 24 (1), 230-253, 2015
872015
A kernelized Stein discrepancy for goodness-of-fit tests
Q Liu, J Lee, M Jordan
International Conference on Machine Learning, 276-284, 2016
862016
Proximal Newton-type methods for convex optimization
JD Lee, Y Sun, M Saunders
Advances in Neural Information Processing Systems, 827-835, 2012
842012
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
752013
Communication-efficient sparse regression
JD Lee, Q Liu, Y Sun, JE Taylor
The Journal of Machine Learning Research 18 (1), 115-144, 2017
66*2017
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima
SS Du, JD Lee, Y Tian, B Poczos, A Singh
arXiv preprint arXiv:1712.00779, 2017
612017
Learning one-hidden-layer neural networks with landscape design
R Ge, JD Lee, T Ma
arXiv preprint arXiv:1711.00501, 2017
612017
l1-regularized neural networks are improperly learnable in polynomial time
Y Zhang, JD Lee, MI Jordan
International Conference on Machine Learning, 993-1001, 2016
592016
Distributed stochastic variance reduced gradient methods by sampling extra data with replacement
JD Lee, Q Lin, T Ma, T Yang
The Journal of Machine Learning Research 18 (1), 4404-4446, 2017
58*2017
Exact post model selection inference for marginal screening
JD Lee, JE Taylor
Advances in Neural Information Processing Systems, 136-144, 2014
582014
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
522017
When is a convolutional filter easy to learn?
SS Du, JD Lee, Y Tian
arXiv preprint arXiv:1709.06129, 2017
522017
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