Important requirements for automotive radar are high resolution, low hardware cost, and
small size. Multiple-input, multiple-output (MIMO) radar technology has been receiving …

The fifth-generation (5G) of cellular networks and beyond requires massive connectivity,
high data rates, and low latency. Millimeter-wave (mmWave) communications is a key 5G …

In exciting recent work, Bertsimas, King and Mazumder (Ann. Statist. 44 (2016) 813–852)
showed that the classical best subset selection problem in regression modeling can be …

Abstract Sparsity in Deep Neural Networks (DNNs) is studied extensively with the focus of
maximizing prediction accuracy given an overall parameter budget. Existing methods rely on …

Big data have enabled decision makers to tailor decisions at the individual level in a variety
of domains, such as personalized medicine and online advertising. Doing so involves …

Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …

Feature selection is important in both machine learning and pattern recognition.
Successfully selecting informative features can significantly increase learning accuracy and …

This paper reviews predictive inference and feature selection for generalized linear models
with scarce but high-dimensional data. We demonstrate that in many cases one can benefit …

This paper gives a review of concentration inequalities which are widely employed in non-
asymptotical analyses of mathematical statistics in a wide range of settings, from distribution …

A conventional wisdom in statistical learning is that large models require strong
regularization to prevent overfitting. Here we show that this rule can be violated by linear …