Estimation of functions from sparse and noisy data is a central theme in machine learning. In
the last few years, many algorithms have been developed that exploit Tikhonov …

The fundamental mathematical tools needed to understand machine learning include linear
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …

The commonly adopted route to control a dynamic system and make it follow the desired
behavior consists of two steps. First, a model of the system is learned from input–output data …

Deep learning is a topic of considerable current interest. The availability of massive data
collections and powerful software resources has led to an impressive amount of results in …

Positive unlabeled (PU) learning is useful in various practical situations, where there is a
need to learn a classifier for a class of interest from an unlabeled data set, which may …

Power spectral density (PSD) maps providing the distribution of RF power across space and
frequency are constructed using power measurements collected by a network of low-cost …

Many statistical estimands can expressed as continuous linear functionals of a conditional
expectation function. This includes the average treatment effect under unconfoundedness …

Abstract Reproducing kernel Hilbert spaces (RKHSs) are key spaces for machine learning
that are becoming popular also for linear system identification. In particular, the so-called …

Learning from examples is one of the key problems in science and engineering. It deals with
function reconstruction from a finite set of direct and noisy samples. Regularization in …

In this paper, we discuss the non-parametric estimate problem using kernel-based LTI
system identification techniques by constructing a Loewner-based interpolant of the …