Mikhail (Misha) Belkin

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Siyuan MaThe Ohio State UniversityVerified email at cse.ohio-state.edu
Vikas SindhwaniGoogle DeepMind RoboticsVerified email at google.com
Chaoyue LiuPurdue University, ECE departmentVerified email at purdue.edu
Daniel HsuColumbia UniversityVerified email at cs.columbia.edu
Adityanarayanan RadhakrishnanBroad Institute of MIT and HarvardVerified email at mit.edu
Yusu WangProfessor, HDSI, Univ. California, San Diego.Verified email at ucsd.edu
Libin ZhuUniversity of WashingtonVerified email at uw.edu
Parthe PanditThakur Family Chair Assistant Professor @ IIT BombayVerified email at iitb.ac.in
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
Kaushik SinhaWichita State UniversityVerified email at wichita.edu
Ulrike von LuxburgUniversity of TübingenVerified email at informatik.uni-tuebingen.de
Olivier BousquetGoogleVerified email at m4x.org
Amirhesam AbedsoltanUniversity of California SanDiegoVerified email at ucsd.edu
Daniel BeagleholePhD Student, UCSDVerified email at ucsd.edu
Jihun HammTulane UniversityVerified email at tulane.edu
Simon DennisProfessor, Melbourne School of Psychological Sciences, University of MelbourneVerified email at unimelb.edu.au
Justin EldridgeAssistant Teaching Professor, University of California, San DiegoVerified email at ucsd.edu
Lorenzo RosascoMaLGa Machine Learning Genoa Center - Università degli Studi di GenovaVerified email at unige.it

Mikhail (Misha) Belkin

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

Verified email at ucsd.edu - Homepage

Machine Learning Learning Theory Artificial Intelligence Interpolation Feature learning


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	10140	2003
Laplacian eigenmaps and spectral techniques for embedding and clustering. M Belkin, P Niyogi Neural Information Processing Systems (NIPS) 14 (14), 585-591, 2001	6380	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	5146	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	2359	2019
Semi-supervised learning on Riemannian manifolds M Belkin, P Niyogi Machine learning 56, 209-239, 2004	1108	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	867	2004
Consistency of spectral clustering U Von Luxburg, M Belkin, O Bousquet The Annals of Statistics, 555-586, 2008	785	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	611	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	562	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	528	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	400	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	388*	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	355	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	352	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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