Matus Telgarsky

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Citations	2582	2274
h-index	15	13
i10-index	16	14

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201220132014201520162017201820192020202110 37 102 124 177 284 439 592 760 16

Co-authors

Daniel HsuColumbia UniversityVerified email at cs.columbia.edu
Ziwei JiUniversity of Illinois Urbana-ChampaignVerified email at illinois.edu
Sham M KakadeUniversity of WashingtonVerified email at cs.washington.edu
Anima AnandkumarCalifornia Institute of Technology and NVIDIAVerified email at caltech.edu
Rong GeDuke UniversityVerified email at cs.duke.edu
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Dylan J. FosterPostdoctoral Fellow, MITVerified email at mit.edu
Maxim RaginskyAssociate Professor of Electrical and Computer Engineering, University of Illinois, Urbana-ChampaignVerified email at illinois.edu
Alexander RakhlinAssociate Professor, MITVerified email at mit.edu
Sanjoy DasguptaProfessor of Computer Science, University of California, San DiegoVerified email at eng.ucsd.edu
Bolton BaileyGraduate Student, University of Illinois, Urbana-ChampaignVerified email at illinois.edu
Andrea VattaniComputer Science PhD from UC San Diego, Chief Scientist at SpiketrapVerified email at cs.ucsd.edu
Robert SchapireMicrosoft ResearchVerified email at microsoft.com
Miroslav DudikMicrosoft ResearchVerified email at microsoft.com
Jacob AbernethyAssistant Professor, University of MichiganVerified email at umich.edu
John LangfordMicrosoft Research New YorkVerified email at hunch.net
Alekh AgarwalMicrosoft ResearchVerified email at microsoft.com
Sébastien LahaieGoogle ResearchVerified email at google.com
Ruta MehtaDept. of Computer Science, University of Illinois at Urbana-ChampaignVerified email at cs.illinois.edu
Alex KuleszaResearch Scientist, GoogleVerified email at google.com

Matus Telgarsky

University of Illinois, Urbana-Champaign

Verified email at illinois.edu - Homepage

deep learning theory machine learning theory


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Tensor decompositions for learning latent variable models A Anandkumar, R Ge, D Hsu, SM Kakade, M Telgarsky Journal of Machine Learning Research 15, 2773-2832, 2014	914	2014
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems, 6240-6249, 2017	503	2017
Benefits of depth in neural networks M Telgarsky arXiv preprint arXiv:1602.04485, 2016	308	2016
Non-convex learning via stochastic gradient Langevin dynamics: a nonasymptotic analysis M Raginsky, A Rakhlin, M Telgarsky arXiv preprint arXiv:1702.03849, 2017	215	2017
Representation benefits of deep feedforward networks M Telgarsky arXiv preprint arXiv:1509.08101, 2015	136	2015
The implicit bias of gradient descent on nonseparable data Z Ji, M Telgarsky Conference on Learning Theory, 1772-1798, 2019	81*	2019
Hartigan’s method: k-means clustering without voronoi M Telgarsky, A Vattani Proceedings of the Thirteenth International Conference on Artificial …, 2010	80	2010
Gradient descent aligns the layers of deep linear networks Z Ji, M Telgarsky arXiv preprint arXiv:1810.02032, 2018	64	2018
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks Z Ji, M Telgarsky arXiv preprint arXiv:1909.12292, 2019	38	2019
Neural networks and rational functions M Telgarsky arXiv preprint arXiv:1706.03301, 2017	38	2017
Agglomerative bregman clustering M Telgarsky, S Dasgupta arXiv preprint arXiv:1206.6446, 2012	32	2012
Margins, shrinkage, and boosting M Telgarsky arXiv preprint arXiv:1303.4172, 2013	30	2013
Tensor decompositions for learning latent variable models (A survey for ALT) A Anandkumar, R Ge, D Hsu, SM Kakade, M Telgarsky International Conference on Algorithmic Learning Theory, 19-38, 2015	20	2015
A primal-dual convergence analysis of boosting M Telgarsky The Journal of Machine Learning Research 13 (1), 561-606, 2012	20	2012
Moment-based Uniform Deviation Bounds for -means and Friends MJ Telgarsky, S Dasgupta Advances in Neural Information Processing Systems 26, 2940-2948, 2013	16	2013
Dirichlet draws are sparse with high probability M Telgarsky arXiv preprint arXiv:1301.4917, 2013	13	2013
Size-noise tradeoffs in generative networks B Bailey, MJ Telgarsky Advances in Neural Information Processing Systems, 6489-6499, 2018	9	2018
A refined primal-dual analysis of the implicit bias Z Ji, M Telgarsky arXiv preprint arXiv:1906.04540, 2019	7	2019
Convex risk minimization and conditional probability estimation M Telgarsky, M Dudik, R Schapire arXiv preprint arXiv:1506.04513, 2015	6	2015
Boosting with the logistic loss is consistent M Telgarsky arXiv preprint arXiv:1305.2648, 2013	6	2013

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