no code implementations • 28 Feb 2023 • Ibrahim Issa, Amedeo Roberto Esposito, Michael Gastpar
We adopt an information-theoretic framework to analyze the generalization behavior of the class of iterative, noisy learning algorithms.
no code implementations • 14 Jan 2020 • Amedeo Roberto Esposito, Michael Gastpar, Ibrahim Issa
The aim of this work is to provide bounds connecting two probability measures of the same event using R\'enyi $\alpha$-Divergences and Sibson's $\alpha$-Mutual Information, a generalization of respectively the Kullback-Leibler Divergence and Shannon's Mutual Information.
no code implementations • 1 Dec 2019 • Amedeo Roberto Esposito, Michael Gastpar, Ibrahim Issa
In this work, the probability of an event under some joint distribution is bounded by measuring it with the product of the marginals instead (which is typically easier to analyze) together with a measure of the dependence between the two random variables.
no code implementations • 5 Mar 2019 • Amedeo Roberto Esposito, Michael Gastpar, Ibrahim Issa
Our contribution consists in the introduction of a new approach, based on the concept of Maximal Leakage, an information-theoretic measure of leakage of information.