3 papers
cs.LG2026
Singular perturbations and hierarchical learning in two-layer neural networks
Cédric Gerbelot, Jean-Christophe Mourrat
We study the population gradient flow of an infinitely wide two-layer neural network learning a misspecified single-index model in high dimension. The two layers are optimized join…
stat.ML2026
Asymptotics of Non-Convex Generalized Linear Models in High-Dimensions: A proof of the replica formula
Matteo Vilucchio, Yatin Dandi, Matéo Pirio Rossignol +2
The analytic characterization of the high-dimensional behavior of optimization for Generalized Linear Models (GLMs) with Gaussian data has been a central focus in statistics and pr…
cs.IT2025
Multi-layer State Evolution Under Random Convolutional Design
Mara Daniels, Cédric Gerbelot, Cédric Gerbelot +3
Signal recovery under generative neural network priors has emerged as a promising direction in statistical inference and computational imaging. Theoretical analysis of reconstructi…