A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …

Data mining techniques often ask for the resolution of optimization problems. Supervised
classification, and, in particular, support vector machines, can be seen as a paradigmatic …

Abstract Machine learning with missing data has been approached in many different ways,
including feature imputation where missing feature values are estimated based on observed …

Robust optimization is still a relatively new approach to optimization problems affected by
uncertainty, but it has already proved so useful in real applications that it is difficult to tackle …

In this paper we survey the primary research, both theoretical and applied, in the area of
robust optimization (RO). Our focus is on the computational attractiveness of RO …

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 …

We derive generalization bounds for learning algorithms based on their robustness: the
property that if a testing sample is “similar” to a training sample, then the testing error is close …

Defect reports are generated from various testing and development activities in software
engineering. Sometimes two reports are submitted that describe the same problem, leading …

We consider regularized support vector machines (SVMs) and show that they are precisely
equivalent to a new robust optimization formulation. We show that this equivalence of robust …

Traditionally, the hinge loss is used to construct support vector machine (SVM) classifiers.
The hinge loss is related to the shortest distance between sets and the corresponding …