Matus Telgarsky

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Ziwei JiGoogle ResearchVerified email at google.com
Daniel HsuColumbia UniversityVerified email at cs.columbia.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
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyVerified email at cs.berkeley.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
Bolton BaileyPh.D. Candidate, University of Illinois, Urbana-ChampaignVerified email at illinois.edu
Clayton SanfordGoogle ResearchVerified email at google.com
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
Jacob AbernethyAssistant Professor, University of MichiganVerified email at umich.edu
Ruta MehtaDept. of Computer Science, University of Illinois at Urbana-ChampaignVerified email at cs.illinois.edu
Sébastien LahaieGoogle ResearchVerified email at google.com
Jingfeng WuUniversity of California, BerkeleyVerified email at berkeley.edu
John LangfordMicrosoft Research New YorkVerified email at hunch.net

Matus Telgarsky

Courant Institute of Mathematical Sciences, New York University

Verified email at nyu.edu - Homepage

deep learning theory machine learning theory


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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	1365	2014
Spectrally-normalized margin bounds for neural networks PL Bartlett, DJ Foster, MJ Telgarsky Advances in neural information processing systems 30, 2017	1361	2017
Benefits of depth in neural networks M Telgarsky Conference on learning theory, 1517-1539, 2016	702	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	584	2017
The implicit bias of gradient descent on nonseparable data Z Ji, M Telgarsky Conference on learning theory, 1772-1798, 2019	341*	2019
Representation benefits of deep feedforward networks M Telgarsky arXiv preprint arXiv:1509.08101, 2015	276	2015
Gradient descent aligns the layers of deep linear networks Z Ji, M Telgarsky arXiv preprint arXiv:1810.02032, 2018	259	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	205	2019
Directional convergence and alignment in deep learning Z Ji, M Telgarsky Advances in Neural Information Processing Systems 33, 17176-17186, 2020	181	2020
Hartigan’s method: k-means clustering without voronoi M Telgarsky, A Vattani Proceedings of the thirteenth international conference on artificial …, 2010	119	2010
Neural networks and rational functions M Telgarsky International Conference on Machine Learning, 3387-3393, 2017	103	2017
Margins, shrinkage, and boosting M Telgarsky International Conference on Machine Learning, 307-315, 2013	91	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	69	2020
Representational strengths and limitations of transformers C Sanford, DJ Hsu, M Telgarsky Advances in Neural Information Processing Systems 36, 2024	68	2024
Characterizing the implicit bias via a primal-dual analysis Z Ji, M Telgarsky Algorithmic Learning Theory, 772-804, 2021	60	2021
Neural tangent kernels, transportation mappings, and universal approximation Z Ji, M Telgarsky, R Xian arXiv preprint arXiv:1910.06956, 2019	57	2019
Early-stopped neural networks are consistent Z Ji, J Li, M Telgarsky Advances in Neural Information Processing Systems 34, 1805-1817, 2021	44	2021
Agglomerative bregman clustering M Telgarsky, S Dasgupta arXiv preprint arXiv:1206.6446, 2012	41	2012
Fast margin maximization via dual acceleration Z Ji, N Srebro, M Telgarsky International Conference on Machine Learning, 4860-4869, 2021	38	2021
Generalization bounds via distillation D Hsu, Z Ji, M Telgarsky, L Wang arXiv preprint arXiv:2104.05641, 2021	35	2021

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