Classification models are often used to make decisions that affect humans: whether to
approve a loan application, extend a job offer, or provide insurance. In such applications …

We present strong mixed-integer programming (MIP) formulations for high-dimensional
piecewise linear functions that correspond to trained neural networks. These formulations …

Abstract Deep Neural Networks (DNNs) are very popular these days, and are the subject of
a very intense investigation. A DNN is made up of layers of internal units (or neurons), each …

The growing range of applications of Machine Learning (ML) in a multitude of settings
motivates the ability of computing small explanations for predictions made. Small …

This paper describes the extensions that were added to the constraint integer programming
framework SCIP in order to enable it to solve convex and nonconvex mixed-integer …

Prediction problems often admit competing models that perform almost equally well. This
effect challenges key assumptions in machine learning when competing models assign …

IEEE 802.1 Q networks with extensions for time-sensitive networking aim to enable
converged networks. Converged networks support hard-real time communication services in …

xG wireless networks require more stringent performance levels. New technologies such as
RIS and RSMA are candidates for meeting some of the performance requirements, including …

This paper proposes a robust classification model, based on support vector machine (SVM),
which simultaneously deals with outliers detection and feature selection. The classifier is …

In this paper we review the relevant literature on mathematical optimization with logical
implications, ie, where constraints can be either active or disabled depending on logical …