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 Liuthe Ohio State UniversityVerified email at osu.edu
Luis RademacherDepartment of Mathematics, UC DavisVerified email at math.ucdavis.edu
James VossGoogleVerified email at google.com
Kaushik SinhaWichita State UniversityVerified email at wichita.edu
Libin ZhuUCSDVerified email at ucsd.edu
Simon DennisProfessor, University of MelbourneVerified email at unimelb.edu.au
Adityanarayanan RadhakrishnanMITVerified email at mit.edu
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
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

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


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Laplacian eigenmaps for dimensionality reduction and data representation M Belkin, P Niyogi Neural computation 15 (6), 1373-1396, 2003	8856	2003
Laplacian eigenmaps and spectral techniques for embedding and clustering. M Belkin, P Niyogi Neural Information Processing Systems (NIPS) 14 (14), 585-591, 2001	5555	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	4562	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	1123	2019
Semi-supervised learning on Riemannian manifolds M Belkin, P Niyogi Machine learning 56 (1), 209-239, 2004	1003	2004
Regularization and semi-supervised learning on large graphs M Belkin, I Matveeva, P Niyogi International Conference on Computational Learning Theory, 624-638, 2004	760	2004
Consistency of spectral clustering U Von Luxburg, M Belkin, O Bousquet The Annals of Statistics, 555-586, 2008	679	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	670	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	580	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	492	2005
Laplacian support vector machines trained in the primal S Melacci, M Belkin Journal of Machine Learning Research 12 (31), 1149−1184, 2011	390	2011
On Manifold Regularization. M Belkin, P Niyogi, V Sindhwani AISTATS, 2005	376	2005
Using Manifold Structure for Partially Labeled Classification M Belkin, P Niyogi NIPS 2002, 2003	349	2003
Convergence of Laplacian eigenmaps M Belkin, P Niyogi Advances in neural information processing systems 19, 2006	334	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	327	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	271	2020
Discrete Laplace operator on meshed surfaces M Belkin, J Sun, Y Wang Proceedings of the twenty-fourth annual symposium on Computational geometry …, 2008	269	2008
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	243	2018
Polynomial learning of distribution families M Belkin, K Sinha SIAM Journal on Computing 44 (4), 889-911, 2015	240*	2015
On learning with integral operators. L Rosasco, M Belkin, E De Vito Journal of Machine Learning Research 11 (2), 2010	240	2010

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