Matus Telgarsky

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Ziwei JiGoogle ResearchVerified email at google.com
Daniel HsuColumbia UniversityVerified email at cs.columbia.edu
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Sham M KakadeHarvard UniversityVerified email at seas.harvard.edu
Anima AnandkumarCalifornia Institute of Technology and NVIDIAVerified email at caltech.edu
Rong GeDuke UniversityVerified email at cs.duke.edu
Dylan J. FosterPrincipal Researcher, Microsoft ResearchVerified email at microsoft.com
Maxim RaginskyProfessor of Electrical and Computer Engineering, University of Illinois, Urbana-ChampaignVerified email at illinois.edu
Alexander RakhlinProfessor, MITVerified email at mit.edu
Clayton SanfordGoogle ResearchVerified email at google.com
Bolton BaileyPh.D. Candidate, University of Illinois, Urbana-ChampaignVerified email at illinois.edu
Robert SchapireMicrosoft ResearchVerified email at microsoft.com
Miroslav DudikMicrosoft ResearchVerified email at microsoft.com
Andrea VattaniML Leadership at Reddit, Computer Science PhD from UC San DiegoVerified email at cs.ucsd.edu
Nathan SrebroProfessor, TTIC and University of ChicagoVerified email at ttic.edu
Jingfeng WuUniversity of California, BerkeleyVerified email at berkeley.edu
Ruta MehtaDept. of Computer Science, University of Illinois at Urbana-ChampaignVerified email at cs.illinois.edu
Jacob AbernethyAssistant Professor, University of MichiganVerified email at umich.edu
John LangfordMicrosoft Research New YorkVerified email at hunch.net
Alekh AgarwalGoogleVerified email at google.com

Matus Telgarsky

Courant Institute of Mathematical Sciences, New York University

Verified email at nyu.edu - Homepage

deep learning theory machine learning theory


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems 30, 2017	1448	2017
Tensor decompositions for learning latent variable models. A Anandkumar, R Ge, DJ Hsu, SM Kakade, M Telgarsky J. Mach. Learn. Res. 15 (1), 2773-2832, 2014	1398	2014
Benefits of depth in neural networks M Telgarsky Conference on learning theory, 1517-1539, 2016	728	2016
Non-convex learning via stochastic gradient langevin dynamics: a nonasymptotic analysis M Raginsky, A Rakhlin, M Telgarsky Conference on Learning Theory, 1674-1703, 2017	623	2017
The implicit bias of gradient descent on nonseparable data Z Ji, M Telgarsky Conference on learning theory, 1772-1798, 2019	357*	2019
Representation benefits of deep feedforward networks M Telgarsky arXiv preprint arXiv:1509.08101, 2015	287	2015
Gradient descent aligns the layers of deep linear networks Z Ji, M Telgarsky arXiv preprint arXiv:1810.02032, 2018	280	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	219	2019
Directional convergence and alignment in deep learning Z Ji, M Telgarsky Advances in Neural Information Processing Systems 33, 17176-17186, 2020	202	2020
Hartigan’s method: k-means clustering without voronoi M Telgarsky, A Vattani Proceedings of the thirteenth international conference on artificial …, 2010	121	2010
Neural networks and rational functions M Telgarsky International Conference on Machine Learning, 3387-3393, 2017	108	2017
Representational strengths and limitations of transformers C Sanford, DJ Hsu, M Telgarsky Advances in Neural Information Processing Systems 36, 36677-36707, 2023	94	2023
Margins, shrinkage, and boosting M Telgarsky International Conference on Machine Learning, 307-315, 2013	93	2013
Gradient descent follows the regularization path for general losses Z Ji, M Dudík, RE Schapire, M Telgarsky Conference on Learning Theory, 2109-2136, 2020	73	2020
Characterizing the implicit bias via a primal-dual analysis Z Ji, M Telgarsky Algorithmic Learning Theory, 772-804, 2021	64	2021
Neural tangent kernels, transportation mappings, and universal approximation Z Ji, M Telgarsky, R Xian arXiv preprint arXiv:1910.06956, 2019	58	2019
Early-stopped neural networks are consistent Z Ji, J Li, M Telgarsky Advances in Neural Information Processing Systems 34, 1805-1817, 2021	50	2021
Generalization bounds via distillation D Hsu, Z Ji, M Telgarsky, L Wang arXiv preprint arXiv:2104.05641, 2021	43	2021
Agglomerative bregman clustering M Telgarsky, S Dasgupta arXiv preprint arXiv:1206.6446, 2012	41	2012
Feature selection and low test error in shallow low-rotation relu networks M Telgarsky The Eleventh International Conference on Learning Representations, 2023	39*	2023

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