Following the success of deep learning in a wide range of applications, neural network-
based machine-learning techniques have received significant interest for accelerating …

Abstract Randomized Neural Networks explore the behavior of neural systems where the
majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical …

We investigate the effect of explicitly enforcing the Lipschitz continuity of neural networks
with respect to their inputs. To this end, we provide a simple technique for computing an …

Abstract Machine learning based systems are reaching society at large and in many aspects
of everyday life. This phenomenon has been accompanied by concerns about the ethical …

We develop a variational framework to understand the properties of the functions learned by
neural networks fit to data. We propose and study a family of continuous-domain linear …

Prototypical examples that best summarize and compactly represent an underlying complex
data distribution, communicate meaningful insights to humans in domains where simple …

We present two methods, based on regression in reproducing kernel Hilbert spaces, for
solving an optimization problem with uncertain parameters for which we have historical data …

This study compares several methods for creating a multiclass, support vector machines-
based (SVM) classifier from a set of binary SVM classifiers. This research aims to identify …

Abstract Spaces with locally varying scale of measurement, like multidimensional structures
with differently scaled dimensions, are pretty common in statistics and machine learning …

The central goal of Algorithmic Fairness is to develop AI-based systems which do not
discriminate subgroups in the population with respect to one or multiple notions of inequity …