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Found 4 papers, 0 papers with code
Generalization Error Bounds for Noisy, Iterative Algorithms via Maximal Leakage
We adopt an information-theoretic framework to analyze the generalization behavior of the class of iterative, noisy learning algorithms.
Robust Generalization via $α$-Mutual Information
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.
Generalization Error Bounds Via Rényi-, $f$-Divergences and Maximal Leakage
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.
A New Approach to Adaptive Data Analysis and Learning via Maximal Leakage
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.
