Cédric Gerbelot | DeepAI

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∙ 10/12/2022

Rigorous dynamical mean field theory for stochastic gradient descent methods

We prove closed-form equations for the exact high-dimensional asymptotic...

0 Cédric Gerbelot, et al. ∙

∙ 05/26/2022

Multi-layer State Evolution Under Random Convolutional Design

Signal recovery under generative neural network priors has emerged as a ...

0 Max Daniels, et al. ∙

∙ 03/22/2022

Learning curves for the multi-class teacher-student perceptron

One of the most classical results in high-dimensional learning theory pr...

17 Elisabetta Cornacchia, et al. ∙

∙ 01/31/2022

Fluctuations, Bias, Variance Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension

From the sampling of data to the initialisation of parameters, randomnes...

6 Bruno Loureiro, et al. ∙

∙ 09/24/2021

Graph-based Approximate Message Passing Iterations

Approximate-message passing (AMP) algorithms have become an important el...

0 Cédric Gerbelot, et al. ∙

∙ 06/07/2021

Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions

Generalised linear models for multi-class classification problems are on...

14 Bruno Loureiro, et al. ∙

∙ 02/16/2021

Capturing the learning curves of generic features maps for realistic data sets with a teacher-student model

Teacher-student models provide a powerful framework in which the typical...

6 Bruno Loureiro, et al. ∙

∙ 06/11/2020

Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (or : How to Prove Kabashima's Replica Formula)

There has been a recent surge of interest in the study of asymptotic rec...

11 Cédric Gerbelot, et al. ∙

∙ 02/11/2020

Asymptotic errors for convex penalized linear regression beyond Gaussian matrices

We consider the problem of learning a coefficient vector x_0 in R^N from...

2 Cédric Gerbelot, et al. ∙

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