Mikhail (Misha) Belkin

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Siyuan MaThe Ohio State UniversityPotvrđena adresa e-pošte na cse.ohio-state.edu
Vikas SindhwaniGoogle DeepMind RoboticsPotvrđena adresa e-pošte na google.com
Chaoyue LiuPurdue University, ECE departmentPotvrđena adresa e-pošte na purdue.edu
Daniel HsuColumbia UniversityPotvrđena adresa e-pošte na cs.columbia.edu
Adityanarayanan RadhakrishnanBroad Institute of MIT and HarvardPotvrđena adresa e-pošte na mit.edu
Yusu WangProfessor, HDSI, Univ. California, San Diego.Potvrđena adresa e-pošte na ucsd.edu
Libin ZhuUniversity of WashingtonPotvrđena adresa e-pošte na uw.edu
Parthe PanditThakur Family Chair Assistant Professor @ IIT BombayPotvrđena adresa e-pošte na iitb.ac.in
Luis RademacherDepartment of Mathematics, UC DavisPotvrđena adresa e-pošte na math.ucdavis.edu
James VossGooglePotvrđena adresa e-pošte na google.com
Caroline UhlerMassachusetts Institute of TechnologyPotvrđena adresa e-pošte na mit.edu
Kaushik SinhaWichita State UniversityPotvrđena adresa e-pošte na wichita.edu
Ulrike von LuxburgUniversity of TübingenPotvrđena adresa e-pošte na informatik.uni-tuebingen.de
Olivier BousquetGooglePotvrđena adresa e-pošte na m4x.org
Amirhesam AbedsoltanUniversity of California SanDiegoPotvrđena adresa e-pošte na ucsd.edu
Daniel BeagleholePhD Student, UCSDPotvrđena adresa e-pošte na ucsd.edu
Jihun HammTulane UniversityPotvrđena adresa e-pošte na tulane.edu
Simon DennisProfessor, Melbourne School of Psychological Sciences, University of MelbournePotvrđena adresa e-pošte na unimelb.edu.au
Justin EldridgeAssistant Teaching Professor, University of California, San DiegoPotvrđena adresa e-pošte na ucsd.edu
Lorenzo RosascoMaLGa Machine Learning Genoa Center - Università degli Studi di GenovaPotvrđena adresa e-pošte na unige.it

Prati

Mikhail (Misha) Belkin

Professor of Data Science, Halıcıoğlu Data Science Institute, CSE, UCSD, Amazon Scholar

Potvrđena adresa e-pošte na ucsd.edu - Početna stranica

Machine Learning Learning Theory Artificial Intelligence Interpolation Feature learning


Naslov Poredaj po navodima Poredaj po godini Poredaj po naslovu	Citirano Citirano	Godina
Laplacian eigenmaps for dimensionality reduction and data representation M Belkin, P Niyogi Neural computation 15 (6), 1373-1396, 2003	10114	2003
Laplacian eigenmaps and spectral techniques for embedding and clustering. M Belkin, P Niyogi Neural Information Processing Systems (NIPS) 14 (14), 585-591, 2001	6361	2001
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. M Belkin, P Niyogi, V Sindhwani Journal of machine learning research 7 (11), 2006	5139	2006
Reconciling modern machine-learning practice and the classical bias–variance trade-off M Belkin, D Hsu, S Ma, S Mandal Proceedings of the National Academy of Sciences 116 (32), 15849-15854, 2019, 2019	2337	2019
Semi-supervised learning on Riemannian manifolds M Belkin, P Niyogi Machine learning 56, 209-239, 2004	1107	2004
Regularization and semi-supervised learning on large graphs M Belkin, I Matveeva, P Niyogi Learning Theory: 17th Annual Conference on Learning Theory, COLT 2004, Banff …, 2004	864	2004
Consistency of spectral clustering U Von Luxburg, M Belkin, O Bousquet The Annals of Statistics, 555-586, 2008	784	2008
Towards a theoretical foundation for Laplacian-based manifold methods M Belkin, P Niyogi Journal of Computer and System Sciences 74 (8), 1289-1308, 2008	761	2008
Beyond the point cloud: from transductive to semi-supervised learning V Sindhwani, P Niyogi, M Belkin Proceedings of the 22nd international conference on Machine learning, 824-831, 2005	610	2005
A co-regularization approach to semi-supervised learning with multiple views V Sindhwani, P Niyogi, M Belkin Proceedings of ICML workshop on learning with multiple views 2005, 74-79, 2005	560	2005
To understand deep learning we need to understand kernel learning M Belkin, S Ma, S Mandal The 35th International Conference on Machine Learning (ICML 2018), 2018	526	2018
Two models of double descent for weak features M Belkin, D Hsu, J Xu SIAM Journal on Mathematics of Data Science 2 (4), 1167-1180, 2020	480	2020
Laplacian support vector machines trained in the primal S Melacci, M Belkin Journal of Machine Learning Research 12 (31), 1149−1184, 2011	433	2011
Convergence of Laplacian eigenmaps M Belkin, P Niyogi Advances in neural information processing systems 19, 2006	399	2006
Loss landscapes and optimization in over-parameterized non-linear systems and neural networks C Liu, L Zhu, M Belkin Applied and Computational Harmonic Analysis 59, 85-116, 2022	387*	2022
Using Manifold Structure for Partially Labeled Classification M Belkin, P Niyogi NIPS 2002, 2003	379	2003
The power of interpolation: Understanding the effectiveness of SGD in modern over-parametrized learning S Ma, R Bassily, M Belkin The 35th International Conference on Machine Learning (ICML 2018), 2018	354	2018
Overfitting or perfect fitting? risk bounds for classification and regression rules that interpolate M Belkin, DJ Hsu, P Mitra Advances in Neural Information Processing Systems (NeurIPS 2018), 2300-2311, 2018	351	2018
On Manifold Regularization. M Belkin, P Niyogi, V Sindhwani AISTATS, 2005	348	2005
On Learning with Integral Operators. L Rosasco, M Belkin, E De Vito Journal of Machine Learning Research 11 (2), 2010	306	2010

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