Publications de Ibrahim Issa | EPFL Graph Search

Total Variation Meets Differential Privacy

The framework of approximate differential privacy is considered, and augmented by leveraging the notion of "the total variation of a (privacy-preserving) mechanism" (denoted by eta-TV). With this refinement, an exact composition result is derived, and show ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2024

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Michael Christoph Gastpar, Ibrahim Issa, Amedeo Roberto Esposito

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. ...

2021

Robust Generalization via $\alpha$-Mutual Information

Michael Christoph Gastpar, Ibrahim Issa, Amedeo Roberto Esposito

The aim of this work is to provide bounds connecting two probability measures of the same event using Rényi

\alpha

-Divergences and Sibson’s

\alpha

-Mutual Information, a generalization of respectively the Kullback-Leibler Divergence and Shannon’s Mutual ...

ETHZ2020

An Operational Approach to Information Leakage

Ibrahim Issa

Given two random variables X and Y , an operational approach is undertaken to quantify the "leakage" of information from X to Y . The resulting measure L (X -> Y) is called maximal leakage, and is defined as the multiplicative increase, upon observing Y , ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Robust Generalization via f−Mutual Information

Michael Christoph Gastpar, Ibrahim Issa, Amedeo Roberto Esposito

Given two probability measures P and Q and an event E, we provide bounds on P(E) in terms of Q(E) and f-divergences. In particular, the bounds are instantiated when the measures considered are a joint distribution and the corresponding product of marginals ...

IEEE2020

Learning and Adaptive Data Analysis via Maximal Leakage

Michael Christoph Gastpar, Ibrahim Issa, Amedeo Roberto Esposito

There has been growing interest in studying connections between generalization error of learning algorithms and information measures. In this work, we generalize a result that employs the maximal leakage, a measure of leakage of information, and explore ho ...

IEEE2019

Strengthened Information-theoretic Bounds on the Generalization Error

Michael Christoph Gastpar, Ibrahim Issa, Amedeo Roberto Esposito

The following problem is considered: given a joint distribution P XY and an event E, bound P XY (E) in terms of P X P Y (E) (where P X P Y is the product of the marginals of P XY ) and a measure of dependence of X and Y. Such bounds have direct application ...

IEEE2019

Computable Bounds on the Exploration Bias

Michael Christoph Gastpar, Ibrahim Issa

Adaptive data analysis is known to introduce bias in reported measurements. Russo and Zou [1] recently introduced an information-theoretic framework to study this problem. Herein, this framework is adopted and new dependence measures are introduced to boun ...

2018