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 …

Abstract Model-based methods are widely used for the fault diagnosis of stochastic dynamic
systems by simply using the input–output relationship of the system. Despite encouraging …

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 …

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 …

Minimax probability machine (MPM) is an interesting discriminative classifier based on
generative prior knowledge. It can directly estimate the probabilistic accuracy bound by …

Deep neural networks have achieved remarkable success in machine learning, computer
vision, and pattern recognition in the last few decades. Recent studies, however, show that …

The goal of regression and classification methods in supervised learning is to minimize the
empirical risk, that is, the expectation of some loss function quantifying the prediction error …

A cascade face detector uses a sequence of node classifiers to distinguish faces from non-
faces. This paper presents a new approach to design node classifiers in the cascade …

Time series prediction, especially financial time series prediction, is a challenging task in
machine learning. In this issue, the data are usually non-stationary and volatile in nature …

Fisher linear discriminant analysis (LDA) can be sensitive to the problem data. Robust
Fisher LDA can systematically alleviate the sensitivity problem by explicitly incorporating a …