Motivated by the fact that there may be inaccuracies in features and labels of training data,
we apply robust optimization techniques to study in a principled way the uncertainty in data …

Machine learning is widely used in security-sensitive settings like spam and malware
detection, although it has been shown that malicious data can be carefully modified at test …

Over the last two decades and with the widespread use of social media and e-commerce
sites, scientific research in the field of sentiment analysis (SA) has made considerable …

In this paper we present novel data-driven optimization models for Support Vector Machines
(SVM), with the aim of linearly separating two sets of points that have non-disjoint convex …

Abstract Machine-learning techniques are widely used in security-related applications, like
spam and malware detection. However, in such settings, they have been shown to be …

In many structured prediction problems, complex relationships between variables are
compactly defined using graphical structures. The most prevalent graphical prediction …

This paper studies unified formulations of support vector machines (SVMs) for binary
classification on the basis of convex analysis, especially, convex risk functions theory, which …

Previous analysis of binary SVMs has demonstrated a deep connection between robustness
to perturbations over uncertainty sets and regularization of the weights. In this paper, we …

In large scale learning problems it is often easy to collect simple statistics of the data, but
hard or impractical to store all the original data. A key question in this setting is how to …

We present formulations for models based on K-nearest neighbors, support vector
machines, and decision trees, and we develop an algorithm OptImpute to find high quality …