Mikhail (Misha) Belkin

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Vikas SindhwaniGoogle DeepMind RoboticsPatvirtintas el. paštas google.com
Siyuan MaThe Ohio State UniversityPatvirtintas el. paštas cse.ohio-state.edu
Chaoyue LiuPurdue University, ECE departmentPatvirtintas el. paštas purdue.edu
Daniel HsuColumbia UniversityPatvirtintas el. paštas cs.columbia.edu
Adityanarayanan RadhakrishnanBroad Institute of MIT and HarvardPatvirtintas el. paštas mit.edu
Yusu WangProfessor, HDSI, Univ. California, San Diego.Patvirtintas el. paštas ucsd.edu
Libin ZhuUniversity of WashingtonPatvirtintas el. paštas uw.edu
Parthe PanditThakur Family Chair Assistant Professor @ IIT BombayPatvirtintas el. paštas iitb.ac.in
Luis RademacherDepartment of Mathematics, UC DavisPatvirtintas el. paštas math.ucdavis.edu
James VossGooglePatvirtintas el. paštas google.com
Caroline UhlerMassachusetts Institute of TechnologyPatvirtintas el. paštas mit.edu
Jihun HammTulane UniversityPatvirtintas el. paštas tulane.edu
Ulrike von LuxburgUniversity of TübingenPatvirtintas el. paštas informatik.uni-tuebingen.de
Olivier BousquetGooglePatvirtintas el. paštas m4x.org
Kaushik SinhaWichita State UniversityPatvirtintas el. paštas wichita.edu
Simon DennisProfessor, Melbourne School of Psychological Sciences, University of MelbournePatvirtintas el. paštas unimelb.edu.au
Daniel BeagleholePhD Student, UCSDPatvirtintas el. paštas ucsd.edu
Qichao QueFacebookPatvirtintas el. paštas fb.com
Lorenzo RosascoMaLGa Machine Learning Genoa Center - Università degli Studi di GenovaPatvirtintas el. paštas unige.it
Ernesto De VitoMaLGa Machine Learning Genoa Center - Università di GenovaPatvirtintas el. paštas unige.it

Stebėti

Mikhail (Misha) Belkin

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

Patvirtintas el. paštas ucsd.edu - Pagrindinis puslapis

Machine Learning Learning Theory Artificial Intelligence Interpolation Feature learning


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Laplacian eigenmaps for dimensionality reduction and data representation M Belkin, P Niyogi Neural computation 15 (6), 1373-1396, 2003	9974	2003
Laplacian eigenmaps and spectral techniques for embedding and clustering. M Belkin, P Niyogi Neural Information Processing Systems (NIPS) 14 (14), 585-591, 2001	6313	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	5097	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	2198	2019
Semi-supervised learning on Riemannian manifolds M Belkin, P Niyogi Machine learning 56, 209-239, 2004	1099	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	862	2004
Consistency of spectral clustering U Von Luxburg, M Belkin, O Bousquet The Annals of Statistics, 555-586, 2008	779	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	739	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	559	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	507	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	465	2020
Laplacian support vector machines trained in the primal S Melacci, M Belkin Journal of Machine Learning Research 12 (31), 1149−1184, 2011	423	2011
Convergence of Laplacian eigenmaps M Belkin, P Niyogi Advances in neural information processing systems 19, 2006	393	2006
Using Manifold Structure for Partially Labeled Classification M Belkin, P Niyogi NIPS 2002, 2003	379	2003
On Manifold Regularization. M Belkin, P Niyogi, V Sindhwani AISTATS, 2005	344	2005
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	340	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	333	2018
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	322*	2022
On Learning with Integral Operators. L Rosasco, M Belkin, E De Vito Journal of Machine Learning Research 11 (2), 2010	297	2010

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