2 citations · 3 across the 7 of their papers we have counts for
5 papers · 1 filter
LDP for the largest eigenvalue of Kronecker random matrices
Alice Guionnet, Jonathan Husson, Jana Reker
We prove a large deviations principle for the largest eigenvalue of Gaussian Kronecker matrices, namely matrices defined as the sum of tensors of independent Gaussian matrices in t…
Global law of conjugate kernel random matrices with heavy-tailed weights
Alice Guionnet, Vanessa Piccolo
We study the asymptotic spectral distribution of the conjugate kernel random matrix , where arises from a two-layer neural network model. We consider the settin…
Large deviations for macroscopic observables of heavy-tailed matrices
Charles Bordenave, Alice Guionnet, Camille Male
We consider a finite collection of independent Hermitian heavy-tailed random matrices of growing dimension. Our model includes the Lévy matrices proposed by Bouchaud and Cizeau, as…
Large deviations for the smallest eigenvalue of a deformed GOE with an outlier
Jeanne Boursier, Alice Guionnet
We establish a large deviation principle for the smallest eigenvalue of a random matrix model composed of the sum of a GOE matrix and a diagonal matrix with an outlier. Our result…
Large deviation principle for the largest eigenvalue of random matrices with a variance profile
Raphaël Ducatez, Alice Guionnet, Jonathan Husson
We establish large deviation principles for the largest eigenvalue of large random matrices with variance profiles. For , we consider random symmetric…