777 citations · 10.3k across the 202 of their papers we have counts for
202 papers
Quenches in the Sherrington-Kirkpatrick model
Vittorio Erba, Freya Behrens, Florent Krzakala +1
The Sherrington-Kirkpatrick (SK) model is a prototype of a complex non-convex energy landscape. Dynamical processes evolving on such landscapes and locally aiming to reach minima a…
Fundamental limits of Non-Linear Low-Rank Matrix Estimation
Pierre Mergny, Justin Ko, Florent Krzakala +1
We consider the task of estimating a low-rank matrix from non-linear and noisy observations. We prove a strong universality result showing that Bayes-optimal performances are chara…
Spectral Phase Transition and Optimal PCA in Block-Structured Spiked models
Pierre Mergny, Justin Ko, Florent Krzakala
We discuss the inhomogeneous spiked Wigner model, a theoretical framework recently introduced to study structured noise in various learning scenarios, through the prism of random m…
Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression
Lucas Clarté, Adrien Vandenbroucque, Guillaume Dalle +3
We investigate popular resampling methods for estimating the uncertainty of statistical models, such as subsampling, bootstrap and the jackknife, and their performance in high-dime…
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs
Kasimir Tanner, Matteo Vilucchio, Bruno Loureiro +1
This work investigates adversarial training in the context of margin-based linear classifiers in the high-dimensional regime where the dimension and the number of data points $…
Asymptotics of feature learning in two-layer networks after one gradient-step
Hugo Cui, Luca Pesce, Yatin Dandi +4
In this manuscript, we investigate the problem of how two-layer neural networks learn features from data, and improve over the kernel regime, after being trained with a single grad…