@conference {654797,
	title = {The role of regularization in classification of high-dimensional noisy Gaussian mixture},
	booktitle = {International Conference on Machine Learning (ICML)},
	year = {2020},
	abstract = {We consider a high-dimensional mixture of two Gaussians in the noisy regime where even an oracle knowing the centers of the clusters misclassifies a small but finite fraction of the points. We provide a rigorous analysis of the generalization error of regularized convex classifiers, including ridge, hinge and logistic regression, in the high-dimensional limit where the number\&nbsp;n\&nbsp;of samples and their dimension\&nbsp;d\&nbsp;go to infinity while their ratio is fixed to\&nbsp;α=n/d. We discuss surprising effects of the regularization that in some cases allows to reach the Bayes-optimal performances. We also illustrate the interpolation peak at low regularization, and analyze the role of the respective sizes of the two clusters.},
	url = {https://arxiv.org/abs/2002.11544},
	author = {F. Mignacco and F. Krzakala and Yue M. Lu and L. Zdeborav{\'a}}
}
