17 citations · 67 across the 16 of their papers we have counts for
6 papers · 1 filter
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…
A non-backtracking method for long matrix and tensor completion
Ludovic Stephan, Yizhe Zhu
We consider the problem of low-rank rectangular matrix completion in the regime where the matrix of size is ``long", i.e., the aspect ratio diverges to infini…
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…