Mikhail (Misha) Belkin

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Siyuan MaThe Ohio State UniversityCorreu electrònic verificat a cse.ohio-state.edu
Vikas SindhwaniGoogle DeepMind RoboticsCorreu electrònic verificat a google.com
Chaoyue LiuPurdue University, ECE departmentCorreu electrònic verificat a purdue.edu
Daniel HsuColumbia UniversityCorreu electrònic verificat a cs.columbia.edu
Adityanarayanan RadhakrishnanBroad Institute of MIT and HarvardCorreu electrònic verificat a mit.edu
Yusu WangProfessor, HDSI, Univ. California, San Diego.Correu electrònic verificat a ucsd.edu
Libin ZhuUniversity of WashingtonCorreu electrònic verificat a uw.edu
Parthe PanditThakur Family Chair Assistant Professor @ IIT BombayCorreu electrònic verificat a iitb.ac.in
Luis RademacherDepartment of Mathematics, UC DavisCorreu electrònic verificat a math.ucdavis.edu
James VossGoogleCorreu electrònic verificat a google.com
Caroline UhlerMassachusetts Institute of TechnologyCorreu electrònic verificat a mit.edu
Jihun HammTulane UniversityCorreu electrònic verificat a tulane.edu
Ulrike von LuxburgUniversity of TübingenCorreu electrònic verificat a informatik.uni-tuebingen.de
Olivier BousquetGoogleCorreu electrònic verificat a m4x.org
Kaushik SinhaWichita State UniversityCorreu electrònic verificat a wichita.edu
Simon DennisProfessor, Melbourne School of Psychological Sciences, University of MelbourneCorreu electrònic verificat a unimelb.edu.au
Daniel BeagleholePhD Student, UCSDCorreu electrònic verificat a ucsd.edu
Qichao QueFacebookCorreu electrònic verificat a fb.com
Justin EldridgeAssistant Teaching Professor, University of California, San DiegoCorreu electrònic verificat a ucsd.edu
Amirhesam AbedsoltanUniversity of California SanDiegoCorreu electrònic verificat a ucsd.edu

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Mikhail (Misha) Belkin

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

Correu electrònic verificat a ucsd.edu - Pàgina d'inici

Machine Learning Learning Theory Artificial Intelligence Interpolation Feature learning


Títol Ordena per cites Ordena per any Ordena per títol	Citada per Citada per	Any
Laplacian eigenmaps for dimensionality reduction and data representation M Belkin, P Niyogi Neural computation 15 (6), 1373-1396, 2003	10061	2003
Laplacian eigenmaps and spectral techniques for embedding and clustering. M Belkin, P Niyogi Neural Information Processing Systems (NIPS) 14 (14), 585-591, 2001	6370	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	5139	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	2282	2019
Semi-supervised learning on Riemannian manifolds M Belkin, P Niyogi Machine learning 56, 209-239, 2004	1104	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	866	2004
Consistency of spectral clustering U Von Luxburg, M Belkin, O Bousquet The Annals of Statistics, 555-586, 2008	787	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	752	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	613	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	516	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	473	2020
Laplacian support vector machines trained in the primal S Melacci, M Belkin Journal of Machine Learning Research 12 (31), 1149−1184, 2011	431	2011
Convergence of Laplacian eigenmaps M Belkin, P Niyogi Advances in neural information processing systems 19, 2006	400	2006
Using Manifold Structure for Partially Labeled Classification M Belkin, P Niyogi NIPS 2002, 2003	381	2003
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	351*	2022
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	349	2018
On Manifold Regularization. M Belkin, P Niyogi, V Sindhwani AISTATS, 2005	345	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	338	2018
On Learning with Integral Operators. L Rosasco, M Belkin, E De Vito Journal of Machine Learning Research 11 (2), 2010	303	2010

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