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 …

A precise high-dimensional asymptotic theory for boosting and minimum-l1-norm
interpolated classifiers Page 1 The Annals of Statistics 2022, Vol. 50, No. 3, 1669–1695 …

Precise statistical analysis of classification accuracies for adversarial training Page 1 The
Annals of Statistics 2022, Vol. 50, No. 4, 2127–2156 https://doi.org/10.1214/22-AOS2180 © …

The curse of overparametrization in adversarial training: Precise analysis of robust
generalization for random features regressi Page 1 The Annals of Statistics 2024, Vol. 52, No. 2 …

Contemporary machine learning applications often involve classification tasks with many
classes. Despite their extensive use, a precise understanding of the statistical properties and …

Universality of regularized regression estimators in high dimensions Page 1 The Annals of
Statistics 2023, Vol. 51, No. 4, 1799–1823 https://doi.org/10.1214/23-AOS2309 © Institute of …

The number of people and the estimation of the population density are one of the important
information concerned by intelligent monitoring. This article reviews, summarizes, and …

Despite the popularity of Empirical Risk Minimization (ERM) algorithms, a theory that
explains their statistical properties in modern high-dimensional regimes is only recently …

Estimation of the average treatment effect (ATE) is a central problem in causal inference. In
recent times, inference for the ATE in the presence of high-dimensional covariates has been …

SLOPE is a relatively new convex optimization procedure for high-dimensional linear
regression via the sorted ℓ 1 penalty: the larger the rank of the fitted coefficient, the larger the …