Mikhail Belkin, Ph.D.
Affiliations: | University of Chicago, Chicago, IL |
Area:
artificial intelligence, pattern recognition, machine learning, computational study of human speech and languageGoogle:
"Mikhail Belkin"Mean distance: 16.01 (cluster 29) | S | N | B | C | P |
Parents
Sign in to add mentor| Partha Niyogi | grad student | 2003 | Chicago | |
| (Problems of learning on manifolds.) | ||||
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Publications
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| Belkin M, Hsu D, Ma S, et al. (2020) Reply to Loog et al.: Looking beyond the peaking phenomenon. Proceedings of the National Academy of Sciences of the United States of America |
| Belkin M, Hsu D, Ma S, et al. (2019) Reconciling modern machine-learning practice and the classical bias-variance trade-off. Proceedings of the National Academy of Sciences of the United States of America |
| Que Q, Belkin M. (2019) Back To The Future: Radial Basis Function Network Revisited. Ieee Transactions On Pattern Analysis and Machine Intelligence |
| Belkin M, Sinha K. (2015) Polynomial learning of distribution families Siam Journal On Computing. 44: 889-911 |
| Zhou X, Belkin M. (2014) Semi-Supervised Learning Academic Press Library in Signal Processing. 1: 1239-1269 |
| Belkin M, Narayanan H, Niyogi P. (2013) Heat flow and a faster algorithm to compute the surface area of a convex body Random Structures and Algorithms. 43: 407-428 |
| Shi T, Belkin M, Yu B. (2009) Data spectroscopy: Eigenspaces of convolution operators and clustering Annals of Statistics. 37: 3960-3984 |
| Luxburg Uv, Belkin M, Bousquet O. (2008) Consistency of spectral clustering Annals of Statistics. 36: 555-586 |
| Belkin M, Niyogi P. (2008) Towards a theoretical foundation for Laplacian-based manifold methods Journal of Computer and System Sciences. 74: 1289-1308 |
| Narayanan H, Belkin M, Niyogi P. (2007) On the relation between low density separation, spectral clustering and graph cuts Advances in Neural Information Processing Systems. 1025-1032 |