Jason M. Klusowski
Jason M. Klusowski
Assistant Professor, Department of Operations Research & Financial Engineering
Verifierad e-postadress på princeton.edu - Startsida
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Risk bounds for high-dimensional ridge function combinations including neural networks
JM Klusowski, AR Barron
arXiv preprint arXiv:1607.01434, 2016
392016
Approximation and estimation for high-dimensional deep learning networks
AR Barron, JM Klusowski
arXiv preprint arXiv:1809.03090, 2018
312018
Approximation by Combinations of ReLU and Squared ReLU Ridge Functions WithandControls
JM Klusowski, AR Barron
IEEE Transactions on Information Theory 64 (12), 7649-7656, 2018
302018
Algorithmic analysis and statistical estimation of slope via approximate message passing
Z Bu, J Klusowski, C Rush, W Su
arXiv preprint arXiv:1907.07502, 2019
162019
Statistical guarantees for estimating the centers of a two-component Gaussian mixture by EM
JM Klusowski, WD Brinda
arXiv preprint arXiv:1608.02280, 2016
132016
Counting motifs with graph sampling
JM Klusowski, Y Wu
Conference On Learning Theory, 1966-2011, 2018
122018
Estimating the coefficients of a mixture of two linear regressions by expectation maximization
JM Klusowski, D Yang, WD Brinda
IEEE Transactions on Information Theory 65 (6), 3515-3524, 2019
112019
Minimax lower bounds for ridge combinations including neural nets
JM Klusowski, AR Barron
2017 IEEE International Symposium on Information Theory (ISIT), 1376-1380, 2017
112017
Uniform approximation by neural networks activated by first and second order ridge splines
JM Klusowski, AR Barron
arXiv preprint arXiv:1607.07819, 2016
112016
Complete analysis of a random forest model
JM Klusowski
arXiv preprint arXiv:1805.02587, 2018
102018
Complexity, statistical risk, and metric entropy of deep nets using total path variation
AR Barron, JM Klusowski
arXiv preprint arXiv:1902.00800, 2019
92019
Estimating the number of connected components in a graph via subgraph sampling
JM Klusowski, Y Wu
Bernoulli 26 (3), 1635-1664, 2020
82020
Estimating the coefficients of a mixture of two linear regressions by expectation maximization
JM Klusowski, D Yang, WD Brinda
arXiv preprint arXiv:1704.08231, 2017
52017
Sharp analysis of a simple model for random forests
JM Klusowski
arXiv preprint arXiv:1805.02587, 2018
42018
Finite-sample risk bounds for maximum likelihood estimation with arbitrary penalties
WD Brinda, JM Klusowski
IEEE Transactions on Information Theory 64 (4), 2727-2741, 2018
42018
Analyzing CART
JM Klusowski
arXiv preprint arXiv:1906.10086, 2019
32019
Estimation of convex supports from noisy measurements
VE Brunel, JM Klusowski, D Yang
arXiv preprint arXiv:1804.09879, 2018
22018
Lecture notes on information theory
G Ajjanagadde, A Makur, J Klusowski, S Xu
22017
Good linear classifiers are abundant in the interpolating regime
R Theisen, JM Klusowski, MW Mahoney
arXiv preprint arXiv:2006.12625, 2020
12020
Global Capacity Measures for Deep ReLU Networks via Path Sampling
R Theisen, JM Klusowski, H Wang, NS Keskar, C Xiong, R Socher
arXiv preprint arXiv:1910.10245, 2019
12019
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