Jason M. Klusowski

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Andrew R BarronProfessor of Statistics, Yale UniversityAdresse e-mail validée de yale.edu
Dana YangAssistant Professor, Cornell UniversityAdresse e-mail validée de cornell.edu
Weijie SuAssociate Professor, University of PennsylvaniaAdresse e-mail validée de wharton.upenn.edu
Cynthia RushColumbia UniversityAdresse e-mail validée de columbia.edu
Zhiqi BuAmazon AIAdresse e-mail validée de sas.upenn.edu
Matias D. CattaneoPrinceton UniversityAdresse e-mail validée de princeton.edu
Yihong WuProfessor, Department of Statistics and Data ScienceAdresse e-mail validée de yale.edu
Ryan TheisenUniversity of California, BerkeleyAdresse e-mail validée de berkeley.edu
Victor-Emmanuel BrunelENSAE-CREST, Professor in StatisticsAdresse e-mail validée de ensae.fr
Michael MahoneyProfessor of Statistics, UC BerkeleyAdresse e-mail validée de stat.berkeley.edu
Rajita ChandakPrinceton UniversityAdresse e-mail validée de princeton.edu
Boris ShigidaPhD Student, Princeton UniversityAdresse e-mail validée de princeton.edu
Caiming XiongSalesforce ResearchAdresse e-mail validée de salesforce.com
Nitish Shirish KeskarOpenAIAdresse e-mail validée de openai.com
Huan WangSalesforce ResearchAdresse e-mail validée de yale.edu
Richard Socheryou.comAdresse e-mail validée de stanford.edu
William G UnderwoodPhD Candidate, ORFE Department, Princeton UniversityAdresse e-mail validée de princeton.edu
Xin ChenPrinceton UniversityAdresse e-mail validée de princeton.edu
Jonathan W. SiegelAssistant Professor, Texas A&M UniversityAdresse e-mail validée de tamu.edu
Yan Shuo TanAssistant Professor in Statistics, National University of SingaporeAdresse e-mail validée de berkeley.edu

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Jason M. Klusowski

Assistant Professor, Department of Operations Research & Financial Engineering

Adresse e-mail validée de princeton.edu - Page d'accueil

statistics probability machine learning information theory


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Approximation by Combinations of ReLU and Squared ReLU Ridge Functions With and Controls JM Klusowski, AR Barron IEEE Transactions on Information Theory 64 (12), 7649-7656, 2018	160*	2018
Approximation and estimation for high-dimensional deep learning networks AR Barron, JM Klusowski arXiv preprint arXiv:1809.03090, 2018	67	2018
Risk bounds for high-dimensional ridge function combinations including neural networks JM Klusowski, AR Barron arXiv preprint arXiv:1607.01434, 2016	67	2016
Algorithmic analysis and statistical estimation of SLOPE via approximate message passing Z Bu, JM Klusowski, C Rush, WJ Su IEEE Transactions on Information Theory 67 (1), 506-537, 2020	53	2020
Sharp analysis of a simple model for random forests J Klusowski International Conference on Artificial Intelligence and Statistics, 757-765, 2021	52*	2021
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	51	2019
Large scale prediction with decision trees JM Klusowski, PM Tian Journal of the American Statistical Association 119 (545), 525-537, 2024	29*	2024
Complexity, statistical risk, and metric entropy of deep nets using total path variation AR Barron, JM Klusowski arXiv preprint arXiv:1902.00800, 2019	27	2019
Counting motifs with graph sampling JM Klusowski, Y Wu Conference On Learning Theory, 1966-2011, 2018	27	2018
Statistical guarantees for estimating the centers of a two-component Gaussian mixture by EM JM Klusowski, WD Brinda arXiv preprint arXiv:1608.02280, 2016	22	2016
Sparse learning with CART J Klusowski Advances in Neural Information Processing Systems 33, 11612-11622, 2020	19	2020
Minimax lower bounds for ridge combinations including neural nets JM Klusowski, AR Barron 2017 IEEE International Symposium on Information Theory (ISIT), 1376-1380, 2017	18	2017
Estimating the number of connected components in a graph via subgraph sampling JM Klusowski, Y Wu	15	2020
Characterizing the SLOPE trade-off: A variational perspective and the Donoho–Tanner limit Z Bu, JM Klusowski, C Rush, WJ Su The Annals of Statistics 51 (1), 33-61, 2023	10	2023
Analyzing cart JM Klusowski arXiv preprint arXiv:1906.10086, 2019	8	2019
On the implicit bias of adam MD Cattaneo, JM Klusowski, B Shigida arXiv preprint arXiv:2309.00079, 2023	7	2023
Estimation of convex supports from noisy measurements VE Brunel, JM Klusowski, D Yang	7	2021
Good classifiers are abundant in the interpolating regime R Theisen, J Klusowski, M Mahoney International Conference on Artificial Intelligence and Statistics, 3376-3384, 2021	7*	2021
Convergence rates of oblique regression trees for flexible function libraries MD Cattaneo, R Chandak, JM Klusowski arXiv preprint arXiv:2210.14429, 2022	5	2022
Nonparametric variable screening with optimal decision stumps J Klusowski, P Tian International Conference on Artificial Intelligence and Statistics, 748-756, 2021	5	2021

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