Internet of Things (IoT) technology is prospering and entering every part of our lives, be it
education, home, vehicles, or healthcare. With the increase in the number of connected …

The concepts of risk-aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. Statistical learning community has also …

This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …

Deep learning has greatly improved visual recognition in recent years. However, recent
research has shown that there exist many adversarial examples that can negatively impact …

This paper concerns the estimation of facial attributes-namely, age and gender-from images
of faces acquired in challenging, in the wild conditions. This problem has received far less …

Several recent works have shown that state-of-the-art classifiers are vulnerable to worst-
case (ie, adversarial) perturbations of the datapoints. On the other hand, it has been …

The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …

In this paper, we survey the primary research on the theory and applications of
distributionally robust optimization (DRO). We start with reviewing the modeling power and …

In many big-data systems, large amounts of information are recorded and stored for
analytics purposes. Often however, this vast amount of information does not offer additional …

Advancing lithium-ion battery technology requires the optimization of cycling protocols. A
new data-driven methodology is demonstrated for rapid, accurate prediction of the cycle life …