Recent years have been marked with the fast-pace diversification and increasing ubiquity of
machine learning applications. Yet, a firm theoretical understanding of the surprising …

This paper presents a succinct derivation of the training and generalization performance of a
variety of high-dimensional ridge regression models using the basic tools of random matrix …

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 …

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 …

We consider the problem of learning a target function corresponding to a single hidden layer
neural network, with a quadratic activation function after the first layer, and random weights …

This paper investigates the asymptotic distribution of the maximum-likelihood estimate
(MLE) in multinomial logistic models in the high-dimensional regime where dimension and …

Maximum margin binary classification is one of the most fundamental algorithms in machine
learning, yet the role of featurization maps and the high-dimensional asymptotics of the …

While classical in many theoretical settings—and in particular in statistical physics-inspired
works—the assumption of Gaussian iid input data is often perceived as a strong limitation in …

We study the problem of transfer learning and fine-tuning in linear models for both
regression and binary classification. In particular, we consider the use of stochastic gradient …

We characterise the learning of a mixture of two clouds of data points with generic centroids
via empirical risk minimisation in the high dimensional regime, under the assumptions of …