Jason M. Klusowski

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Andrew R BarronProfessor of Statistics, Yale UniversityVerified email at yale.edu
Dana YangAssistant Professor, Cornell UniversityVerified email at cornell.edu
Weijie SuAssociate Professor, University of PennsylvaniaVerified email at wharton.upenn.edu
Cynthia RushColumbia UniversityVerified email at columbia.edu
Zhiqi BuAmazon AIVerified email at sas.upenn.edu
Matias D. CattaneoPrinceton UniversityVerified email at princeton.edu
Yihong WuProfessor, Department of Statistics and Data ScienceVerified email at yale.edu
Ryan TheisenUniversity of California, BerkeleyVerified email at berkeley.edu
Michael MahoneyProfessor of Statistics, UC BerkeleyVerified email at stat.berkeley.edu
Rajita ChandakEPFLVerified email at epfl.ch
Victor-Emmanuel BrunelENSAE-CREST, Professor in StatisticsVerified email at ensae.fr
Boris ShigidaPhD Student, Princeton UniversityVerified email at princeton.edu
William G UnderwoodUniversity of CambridgeVerified email at cam.ac.uk
Jianqing FanProfessor of Statistics, Professor of Finance, Princeton UniversityVerified email at princeton.edu
Caiming XiongSalesforce ResearchVerified email at salesforce.com
Nitish Shirish KeskarOpenAIVerified email at openai.com
Huan WangSalesforce ResearchVerified email at yale.edu
Richard Socheryou.comVerified email at stanford.edu
Xin ChenPrinceton UniversityVerified email at princeton.edu
Jonathan W. SiegelAssistant Professor, Texas A&M UniversityVerified email at tamu.edu

Jason M. Klusowski

Assistant Professor, Department of Operations Research & Financial Engineering

Verified email at princeton.edu - Homepage

statistics probability machine learning information theory


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	177*	2018
Risk bounds for high-dimensional ridge function combinations including neural networks JM Klusowski, AR Barron arXiv preprint arXiv:1607.01434, 2016	74	2016
Approximation and estimation for high-dimensional deep learning networks AR Barron, JM Klusowski arXiv preprint arXiv:1809.03090, 2018	70	2018
Sharp analysis of a simple model for random forests J Klusowski International Conference on Artificial Intelligence and Statistics, 757-765, 2021	64*	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	59	2019
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	56	2020
Large scale prediction with decision trees JM Klusowski, PM Tian Journal of the American Statistical Association 119 (545), 525-537, 2024	43*	2024
Complexity, statistical risk, and metric entropy of deep nets using total path variation AR Barron, JM Klusowski arXiv preprint arXiv:1902.00800, 2019	31	2019
Counting motifs with graph sampling JM Klusowski, Y Wu Conference On Learning Theory, 1966-2011, 2018	30	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	25	2016
Sparse learning with CART J Klusowski Advances in Neural Information Processing Systems 33, 11612-11622, 2020	23	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	16	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	11	2023
On the implicit bias of adam MD Cattaneo, JM Klusowski, B Shigida arXiv preprint arXiv:2309.00079, 2023	10	2023
Analyzing cart JM Klusowski arXiv preprint arXiv:1906.10086, 2019	10	2019
Good classifiers are abundant in the interpolating regime R Theisen, J Klusowski, M Mahoney International Conference on Artificial Intelligence and Statistics, 3376-3384, 2021	9*	2021
Inference with mondrian random forests MD Cattaneo, JM Klusowski, WG Underwood arXiv preprint arXiv:2310.09702, 2023	8	2023
Convergence rates of oblique regression trees for flexible function libraries MD Cattaneo, R Chandak, JM Klusowski The Annals of Statistics 52 (2), 466-490, 2024	7	2024
Estimation of convex supports from noisy measurements VE Brunel, JM Klusowski, D Yang	7	2021

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