Aggregated predictors are obtained by making a set of basic predictors vote according to
some weights, that is, to some probability distribution. Randomized predictors are obtained …

Fueled by the availability of more data and computing power, recent breakthroughs in cloud-
based machine learning (ML) have transformed every aspect of our lives from face …

The contraction inequality for Rademacher averages is extended to Lipschitz functions with
vector-valued domains, and it is also shown that in the bounding expression the …

We give a basic introduction to Gaussian Process regression models. We focus on
understanding the role of the stochastic process and how it is used to define a distribution …

Self-supervised learning (SSL) has emerged as a powerful framework to learn
representations from raw data without supervision. Yet in practice, engineers face issues …

We describe the maximum-likelihood parameter estimation problem and how the
Expectation-Maximization (EM) algorithm can be used for its solution. We first describe the …

A comprehensive and cutting-edge introduction to the foundations and modern applications
of learning theory. Research has exploded in the field of machine learning resulting in …

Recently developed methods for learning sparse classifiers are among the state-of-the-art in
supervised learning. These methods learn classifiers that incorporate weighted sums of …

In this paper, it is shown how to extract a hypothesis with small risk from the ensemble of
hypotheses generated by an arbitrary on-line learning algorithm run on an independent and …

We exhibit a strong link between frequentist PAC-Bayesian bounds and the Bayesian
marginal likelihood. That is, for the negative log-likelihood loss function, we show that the …