Mikhail (Misha) Belkin

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Siyuan MaThe Ohio State UniversityVerified email at cse.ohio-state.edu
Vikas SindhwaniRobotics at Google, New YorkVerified email at google.com
Daniel HsuColumbia UniversityVerified email at cs.columbia.edu
Yusu WangProfessor, HDSI, Univ. California, San Diego.Verified email at ucsd.edu
Chaoyue LiuUniversity of California, San DiegoVerified email at ucsd.edu
Libin ZhuUCSDVerified email at ucsd.edu
Adityanarayanan RadhakrishnanHarvard UniversityVerified email at seas.harvard.edu
Luis RademacherDepartment of Mathematics, UC DavisVerified email at math.ucdavis.edu
James VossGoogleVerified email at google.com
Caroline UhlerMassachusetts Institute of TechnologyVerified email at mit.edu
Simon DennisProfessor, Melbourne School of Psychological Sciences, University of MelbourneVerified email at unimelb.edu.au
Ulrike von LuxburgUniversity of TübingenVerified email at informatik.uni-tuebingen.de
Olivier BousquetGoogleVerified email at m4x.org
Jihun HammTulane UniversityVerified email at tulane.edu
Kaushik SinhaWichita State UniversityVerified email at wichita.edu
Justin EldridgePhD Student, Computer Science, The Ohio State UniversityVerified email at cse.ohio-state.edu
Lorenzo RosascoMaLGa Machine Learning Genoa Center - Università degli Studi di GenovaVerified email at unige.it
Ernesto De VitoMaLGa Machine Learning Genoa Center - Università di GenovaVerified email at unige.it
Parthe PanditThakur Family Chair Assistant Professor @ IIT BombayVerified email at iitb.ac.in
Qichao QueFacebookVerified email at fb.com

Mikhail (Misha) Belkin

Professor of Data Science, Halıcıoğlu Data Science Institute, Computer Scinece and Engineering, UCSD

Verified email at ucsd.edu - Homepage

Machine Learning Learning Theory Artificial Intelligence Interpolation Feature learning


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Laplacian eigenmaps for dimensionality reduction and data representation M Belkin, P Niyogi Neural computation 15 (6), 1373-1396, 2003	9442	2003
Laplacian eigenmaps and spectral techniques for embedding and clustering. M Belkin, P Niyogi Neural Information Processing Systems (NIPS) 14 (14), 585-591, 2001	5996	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	4859	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	1685	2019
Semi-supervised learning on Riemannian manifolds M Belkin, P Niyogi Machine learning 56, 209-239, 2004	1057	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	843	2004
Consistency of spectral clustering U Von Luxburg, M Belkin, O Bousquet The Annals of Statistics, 555-586, 2008	730	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	706	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	595	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	529	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	418	2018
Laplacian support vector machines trained in the primal S Melacci, M Belkin Journal of Machine Learning Research 12 (31), 1149−1184, 2011	410	2011
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	373	2020
Convergence of Laplacian eigenmaps M Belkin, P Niyogi Advances in neural information processing systems 19, 2006	369	2006
Using Manifold Structure for Partially Labeled Classification M Belkin, P Niyogi NIPS 2002, 2003	362	2003
On Manifold Regularization. M Belkin, P Niyogi, V Sindhwani AISTATS, 2005	336	2005
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	306	2018
Discrete Laplace operator on meshed surfaces M Belkin, J Sun, Y Wang Proceedings of the twenty-fourth annual symposium on Computational geometry …, 2008	280	2008
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	278	2018
On learning with integral operators. L Rosasco, M Belkin, E De Vito Journal of Machine Learning Research 11 (2), 2010	271	2010

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