Takip et
Mikhail (Misha) Belkin
Mikhail (Misha) Belkin
Professor of Data Science, Halıcıoğlu Data Science Institute, CSE, UCSD, Amazon Scholar
ucsd.edu üzerinde doğrulanmış e-posta adresine sahip - Ana Sayfa
Başlık
Alıntı yapanlar
Alıntı yapanlar
Yıl
Laplacian eigenmaps for dimensionality reduction and data representation
M Belkin, P Niyogi
Neural computation 15 (6), 1373-1396, 2003
101092003
Laplacian eigenmaps and spectral techniques for embedding and clustering.
M Belkin, P Niyogi
Neural Information Processing Systems (NIPS) 14 (14), 585-591, 2001
63612001
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
51372006
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
23272019
Semi-supervised learning on Riemannian manifolds
M Belkin, P Niyogi
Machine learning 56, 209-239, 2004
11072004
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
8642004
Consistency of spectral clustering
U Von Luxburg, M Belkin, O Bousquet
The Annals of Statistics, 555-586, 2008
7842008
Towards a theoretical foundation for Laplacian-based manifold methods
M Belkin, P Niyogi
Journal of Computer and System Sciences 74 (8), 1289-1308, 2008
7592008
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
6102005
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
5602005
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
5262018
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
4792020
Laplacian support vector machines trained in the primal
S Melacci, M Belkin
Journal of Machine Learning Research 12 (31), 1149−1184, 2011
4332011
Convergence of Laplacian eigenmaps
M Belkin, P Niyogi
Advances in neural information processing systems 19, 2006
3982006
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
383*2022
Using Manifold Structure for Partially Labeled Classification
M Belkin, P Niyogi
NIPS 2002, 2003
3792003
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
3522018
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
3492018
On Manifold Regularization.
M Belkin, P Niyogi, V Sindhwani
AISTATS, 2005
3482005
On Learning with Integral Operators.
L Rosasco, M Belkin, E De Vito
Journal of Machine Learning Research 11 (2), 2010
3062010
Sistem, işlemi şu anda gerçekleştiremiyor. Daha sonra yeniden deneyin.
Makaleler 1–20