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
Yusu WangProfessor, HDSI, Univ. California, San Diego.Verified email at ucsd.edu
Daniel HsuColumbia UniversityVerified email at cs.columbia.edu
Luis RademacherDepartment of Mathematics, UC DavisVerified email at math.ucdavis.edu
James VossGoogleVerified email at google.com
Chaoyue Liuthe Ohio State UniversityVerified email at osu.edu
Kaushik SinhaWichita State UniversityVerified email at wichita.edu
Simon DennisProfessor, 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
Caroline UhlerMassachusetts Institute of TechnologyVerified email at mit.edu
Adityanarayanan RadhakrishnanMITVerified email at mit.edu
jian sunProfessor, Tsinghua UniversityVerified email at math.tsinghua.edu.cn
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
Xueyuan ZhouPhD, Computer Science, University of ChicagoVerified email at cs.uchicago.edu
Stefano MelacciAssociate Professor, University of Siena, ItalyVerified email at diism.unisi.it

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	8551	2003
Laplacian eigenmaps and spectral techniques for embedding and clustering. M Belkin, P Niyogi Neural Information Processing Systems (NIPS) 14 (14), 585-591, 2001	5390	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	4426	2006
Semi-supervised learning on Riemannian manifolds M Belkin, P Niyogi Machine learning 56 (1), 209-239, 2004	980	2004
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	888	2019
Regularization and semi-supervised learning on large graphs M Belkin, I Matveeva, P Niyogi International Conference on Computational Learning Theory, 624-638, 2004	741	2004
Consistency of spectral clustering U Von Luxburg, M Belkin, O Bousquet The Annals of Statistics, 555-586, 2008	641	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	636	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	572	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	481	2005
Laplacian support vector machines trained in the primal S Melacci, M Belkin Journal of Machine Learning Research 12 (31), 1149−1184, 2011	387	2011
On Manifold Regularization. M Belkin, P Niyogi, V Sindhwani AISTATS, 2005	367	2005
Using Manifold Structure for Partially Labeled Classification M Belkin, P Niyogi NIPS 2002, 2003	345	2003
Convergence of Laplacian eigenmaps M Belkin, P Niyogi Advances in neural information processing systems 19, 2006	313	2006
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	283	2018
Discrete Laplace operator on meshed surfaces M Belkin, J Sun, Y Wang Proceedings of the twenty-fourth annual symposium on Computational geometry …, 2008	264	2008
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	230	2020
Polynomial learning of distribution families M Belkin, K Sinha SIAM Journal on Computing 44 (4), 889-911, 2015	224*	2015
On learning with integral operators. L Rosasco, M Belkin, E De Vito Journal of Machine Learning Research 11 (2), 2010	217	2010
Constructing Laplace Operator from Point Clouds in ℝ^d M Belkin, J Sun, Y Wang Proceedings of the twentieth annual ACM-SIAM symposium on Discrete …, 2009	204	2009

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