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Minimax Supervised Clustering in the Anisotropic Gaussian Mixture Model:
  A new take on Robust Interpolation

Minimax Supervised Clustering in the Anisotropic Gaussian Mixture Model: A new take on Robust Interpolation

13 November 2021
Stanislav Minsker
M. Ndaoud
Yiqiu Shen
ArXiv (abs)PDFHTML

Papers citing "Minimax Supervised Clustering in the Anisotropic Gaussian Mixture Model: A new take on Robust Interpolation"

3 / 3 papers shown
Title
Sharp-SSL: Selective high-dimensional axis-aligned random projections
  for semi-supervised learning
Sharp-SSL: Selective high-dimensional axis-aligned random projections for semi-supervised learningJournal of the American Statistical Association (JASA), 2023
Tengyao Wang
Guang Cheng
M. Gataric
R. Samworth
189
1
0
18 Apr 2023
Interpolating Discriminant Functions in High-Dimensional Gaussian Latent
  Mixtures
Interpolating Discriminant Functions in High-Dimensional Gaussian Latent MixturesBiometrika (Biometrika), 2022
Xin Bing
M. Wegkamp
177
2
0
25 Oct 2022
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear DataAnnual Conference Computational Learning Theory (COLT), 2022
Spencer Frei
Niladri S. Chatterji
Peter L. Bartlett
MLT
424
87
0
11 Feb 2022
1