The rapid recent progress in machine learning (ML) has raised a number of scientific
questions that challenge the longstanding dogma of the field. One of the most important …

Deep learning methods operate in regimes that defy the traditional statistical mindset.
Neural network architectures often contain more parameters than training samples, and are …

Interpolators—estimators that achieve zero training error—have attracted growing attention
in machine learning, mainly because state-of-the art neural networks appear to be models of …

The class of random features is one of the most popular techniques to speed up kernel
methods in large-scale problems. Related works have been recognized by the NeurIPS Test …

We consider a model for logistic regression where only a subset of features of size is used
for training a linear classifier over training samples. The classifier is obtained by running …

We consider the linear model $\vy=\vX\vbeta_ {\star}+\vepsilon $ with $\vX\in\mathbb
{R}^{n\times p} $ in the overparameterized regime $ p> n $. We estimate $\vbeta_ {\star} …

Classical learning theory suggests that the optimal generalization performance of a machine
learning model should occur at an intermediate model complexity, with simpler models …

Recent empirical and theoretical studies have shown that many learning algorithms--from
linear regression to neural networks--can have test performance that is non-monotonic in …

We prove bounds on the population risk of the maximum margin algorithm for two-class
linear classification. For linearly separable training data, the maximum margin algorithm has …

A significant obstacle in the development of robust machine learning models is\emph
{covariate shift}, a form of distribution shift that occurs when the input distributions of the …