Optimal errors and phase transitions in high-dimensional generalized linear models

Generalized linear models (GLMs) are used in high-dimensional machine learning, statistics, communications, and signal processing. In this paper we analyze GLMs when the data matrix is random, as relevant in problems such as compressed sensing, error-correcting codes, or benchmark models in neural n...

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Bibliographic Details

Published in:	Proceedings of the National Academy of Sciences - PNAS Vol. 116; no. 12; p. 5451
Main Authors:	Barbier, Jean, Krzakala, Florent, Macris, Nicolas, Miolane, Léo, Zdeborová, Lenka
Format:	Journal Article
Language:	English
Published:	United States 19.03.2019
Subjects:	generalized linear model high-dimensional inference approximate message-passing algorithm perceptron Bayesian inference
ISSN:	1091-6490, 1091-6490
Online Access:	Get more information
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