82 citations · 360 across the 42 of their papers we have counts for
8 papers · 1 filter
High-dimensional robust regression under heavy-tailed data: Asymptotics and Universality
Urte Adomaityte, Leonardo Defilippis, Bruno Loureiro +1
We investigate the high-dimensional properties of robust regression estimators in the presence of heavy-tailed contamination of both the covariates and response functions. In parti…
Escaping mediocrity: how two-layer networks learn hard generalized linear models with SGD
Luca Arnaboldi, Florent Krzakala, Bruno Loureiro +1
This study explores the sample complexity for two-layer neural networks to learn a generalized linear target function under Stochastic Gradient Descent (SGD), focusing on the chall…
How Two-Layer Neural Networks Learn, One (Giant) Step at a Time
Yatin Dandi, Florent Krzakala, Bruno Loureiro +2
For high-dimensional Gaussian data, we investigate theoretically how the features of a two-layer neural network adapt to the structure of the target function through a few large ba…
Expectation consistency for calibration of neural networks
Lucas Clarté, Bruno Loureiro, Florent Krzakala +1
Despite their incredible performance, it is well reported that deep neural networks tend to be overoptimistic about their prediction confidence. Finding effective and efficient cal…
Universality laws for Gaussian mixtures in generalized linear models
Yatin Dandi, Ludovic Stephan, Florent Krzakala +2
Let denote independent samples from a general mixture distribution , and consider the hypothesis class of gener…
Are Gaussian data all you need? Extents and limits of universality in high-dimensional generalized linear estimation
Luca Pesce, Florent Krzakala, Bruno Loureiro +1
In this manuscript we consider the problem of generalized linear estimation on Gaussian mixture data with labels given by a single-index model. Our first result is a sharp asymptot…
From high-dimensional & mean-field dynamics to dimensionless ODEs: A unifying approach to SGD in two-layers networks
Luca Arnaboldi, Ludovic Stephan, Florent Krzakala +1
This manuscript investigates the one-pass stochastic gradient descent (SGD) dynamics of a two-layer neural network trained on Gaussian data and labels generated by a similar, thoug…
Deterministic equivalent and error universality of deep random features learning
Dominik Schröder, Hugo Cui, Daniil Dmitriev +1
This manuscript considers the problem of learning a random Gaussian network function using a fully connected network with frozen intermediate layers and trainable readout layer. Th…