Teacher-student models provide a framework in which the typical-case performance of high-
dimensional supervised learning can be described in closed form. The assumptions of …

This book presents a unified theory of random matrices for applications in machine learning,
offering a large-dimensional data vision that exploits concentration and universality …

The notion of task similarity is at the core of various machine learning paradigms, such as
domain adaptation and meta-learning. Current methods to quantify it are often heuristic …

We study generalised linear regression and classification for a synthetically generated
dataset encompassing different problems of interest, such as learning with random features …

We prove a universality theorem for learning with random features. Our result shows that, in
terms of training and generalization errors, a random feature model with a nonlinear …

Understanding the reasons for the success of deep neural networks trained using stochastic
gradient-based methods is a key open problem for the nascent theory of deep learning. The …

Understanding the impact of data structure on the computational tractability of learning is a
key challenge for the theory of neural networks. Many theoretical works do not explicitly …

This article characterizes the exact asymptotics of random Fourier feature (RFF) regression,
in the realistic setting where the number of data samples $ n $, their dimension $ p $, and …

Generalised linear models for multi-class classification problems are one of the fundamental
building blocks of modern machine learning tasks. In this manuscript, we characterise the …

A recent line of work in high-dimensional statistics working under the Gaussian mixture
hypothesis has led to a number of results in the context of empirical risk minimization …