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 …

We investigate theoretically how the features of a two-layer neural network adapt to the
structure of the target function through a few large batch gradient descent steps, leading to …

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 …

We consider linear regression problems with a varying number of random projections,
where we provably exhibit a double descent curve for a fixed prediction problem, with a high …

On a variety of tasks, the performance of neural networks predictably improves with training
time, dataset size and model size across many orders of magnitude. This phenomenon is …

Uncertainty quantification is a central challenge in reliable and trustworthy machine
learning. Naive measures such as last-layer scores are well-known to yield overconfident …

We establish precise structural and risk equivalences between subsampling and ridge
regularization for ensemble ridge estimators. Specifically, we prove that linear and quadratic …

The recent progresses in Machine Learning opened the door to actual applications of
learning algorithms but also to new research directions both in the field of Machine Learning …

We study subsampling-based ridge ensembles in the proportional asymptotics regime,
where the feature size grows proportionally with the sample size such that their ratio …

Artificial networks have been studied through the prism of statistical mechanics as
disordered systems since the 1980s, starting from the simple models of Hopfield's …