Mikhail (Misha) Belkin

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Vikas SindhwaniRobotics at Google, New YorkVerified email at google.com
Siyuan MaThe Ohio State UniversityVerified email at cse.ohio-state.edu
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
Ulrike von LuxburgUniversity of TübingenVerified email at informatik.uni-tuebingen.de
Olivier BousquetGoogleVerified email at m4x.org
Luis RademacherDepartment of Mathematics, UC DavisVerified email at math.ucdavis.edu
James VossGoogleVerified email at google.com
Libin ZhuUCSDVerified email at ucsd.edu
Adityanarayanan RadhakrishnanMITVerified email at mit.edu
Simon DennisProfessor, University of MelbourneVerified email at unimelb.edu.au
Jihun HammTulane UniversityVerified email at tulane.edu
Kaushik SinhaWichita State UniversityVerified email at wichita.edu
Caroline UhlerMassachusetts Institute of TechnologyVerified email at mit.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
Qichao QueFacebookVerified email at fb.com
Xueyuan ZhouPhD, Computer Science, University of ChicagoVerified email at cs.uchicago.edu

Mikhail (Misha) Belkin

Professor of Data Science, Halıcıoğlu Data Science Institute, UCSD

Verified email at ucsd.edu - Homepage

Machine Learning Learning Theory Artificial Intelligence Interpolation


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Laplacian eigenmaps for dimensionality reduction and data representation M Belkin, P Niyogi Neural computation 15 (6), 1373-1396, 2003	9128	2003
Laplacian eigenmaps and spectral techniques for embedding and clustering. M Belkin, P Niyogi Neural Information Processing Systems (NIPS) 14 (14), 585-591, 2001	5811	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	4704	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	1407	2019
Semi-supervised learning on Riemannian manifolds M Belkin, P Niyogi Machine learning 56, 209-239, 2004	1030	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	788	2004
Consistency of spectral clustering U Von Luxburg, M Belkin, O Bousquet The Annals of Statistics, 555-586, 2008	704	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	689	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	592	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	513	2005
Laplacian support vector machines trained in the primal S Melacci, M Belkin Journal of Machine Learning Research 12 (31), 1149−1184, 2011	400	2011
On Manifold Regularization. M Belkin, P Niyogi, V Sindhwani AISTATS, 2005	380	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	374	2018
Using Manifold Structure for Partially Labeled Classification M Belkin, P Niyogi NIPS 2002, 2003	356	2003
Convergence of Laplacian eigenmaps M Belkin, P Niyogi Advances in neural information processing systems 19, 2006	349	2006
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	321	2020
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	273	2018
Discrete Laplace operator on meshed surfaces M Belkin, J Sun, Y Wang Proceedings of the twenty-fourth annual symposium on Computational geometry …, 2008	273	2008
On learning with integral operators. L Rosasco, M Belkin, E De Vito Journal of Machine Learning Research 11 (2), 2010	257	2010
Polynomial learning of distribution families M Belkin, K Sinha SIAM Journal on Computing 44 (4), 889-911, 2015	245*	2015

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