Generalized linear models (GLMs) are used in high-dimensional machine learning,
statistics, communications, and signal processing. In this paper we analyze GLMs when the …

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 …

In this work, we study the transfer learning problem under high-dimensional generalized
linear models (GLMs), which aim to improve the fit on target data by borrowing information …

For large scale learning problems, it is desirable if we can obtain the optimal model
parameters by going through the data in only one pass. Polyak and Juditsky (1992) showed …

Generalised linear models for multi-class classification problems are one of the fundamental
building blocks of modern machine learning tasks. In this manuscript, we characterise the …

Logistic regression is commonly used for modeling dichotomous outcomes. In the classical
setting, where the number of observations is much larger than the number of parameters …

We prove a new generalization bound that shows for any class of linear predictors in
Gaussian space, the Rademacher complexity of the class and the training error under any …

At the heart of machine learning lies the question of generalizability of learned rules over
previously unseen data. While over-parameterized models based on neural networks are …

We establish novel generalization bounds for learning algorithms that converge to global
minima. We derive black-box stability results that only depend on the convergence of a …

Consider the high-dimensional linear regression model y= X β*+ w, where y∈\BBR n is an
observation vector, X∈\BBR n× d is a design matrix with d>; n, β*∈\BBR d is an unknown …