| Laplacian eigenmaps for dimensionality reduction and data representation M Belkin, P Niyogi Neural computation 15 (6), 1373-1396, 2003 | 9728 | 2003 |
| Laplacian eigenmaps and spectral techniques for embedding and clustering. M Belkin, P Niyogi Neural Information Processing Systems (NIPS) 14 (14), 585-591, 2001 | 6186 | 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 | 4998 | 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 | 1967 | 2019 |
| Semi-supervised learning on Riemannian manifolds M Belkin, P Niyogi Machine learning 56, 209-239, 2004 | 1080 | 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 | 847 | 2004 |
| Consistency of spectral clustering U Von Luxburg, M Belkin, O Bousquet The Annals of Statistics, 555-586, 2008 | 748 | 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 | 724 | 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 | 603 | 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 | 552 | 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 | 467 | 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 | 426 | 2020 |
| Laplacian support vector machines trained in the primal S Melacci, M Belkin Journal of Machine Learning Research 12 (31), 1149−1184, 2011 | 418 | 2011 |
| Convergence of Laplacian eigenmaps M Belkin, P Niyogi Advances in neural information processing systems 19, 2006 | 381 | 2006 |
| Using Manifold Structure for Partially Labeled Classification M Belkin, P Niyogi NIPS 2002, 2003 | 373 | 2003 |
| On Manifold Regularization. M Belkin, P Niyogi, V Sindhwani AISTATS, 2005 | 341 | 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 | 320 | 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 | 319 | 2018 |
| Discrete Laplace operator on meshed surfaces M Belkin, J Sun, Y Wang Proceedings of the twenty-fourth annual symposium on Computational geometry …, 2008 | 290 | 2008 |
| On Learning with Integral Operators. L Rosasco, M Belkin, E De Vito Journal of Machine Learning Research 11 (2), 2010 | 287 | 2010 |
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