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 …

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 …

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

Evaluating the performance of machine learning models under distribution shifts is
challenging, especially when we only have unlabeled data from the shifted (target) domain …

We study the asymptotic generalization of an overparameterized linear model for multiclass
classification under the Gaussian covariates bi-level model introduced in Subramanian et …

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 …

For a large class of feature maps we provide a tight asymptotic characterisation of the test
error associated with learning the readout layer, in the high-dimensional limit where the …

We study the behavior of optimal ridge regularization and optimal ridge risk for out-of-
distribution prediction, where the test distribution deviates arbitrarily from the train …

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 …

Neural networks extract features from data using stochastic gradient descent (SGD). In
particular, higher-order input cumulants (HOCs) are crucial for their performance. However …