no code implementations • 19 Apr 2022 • Eustasio del Barrio, Alberto Gonzalez-Sanz, Jean-Michel Loubes, Jonathan Niles-Weed
We prove a central limit theorem for the entropic transportation cost between subgaussian probability measures, centered at the population cost.
1 code implementation • CVPR 2021 • Mathieu Serrurier, Franck Mamalet, Alberto González-Sanz, Thibaut Boissin, Jean-Michel Loubes, Eustasio del Barrio
This loss function has a direct interpretation in terms of adversarial robustness together with certifiable robustness bound.
no code implementations • 9 Jun 2020 • Eustasio del Barrio, Jean-Michel Loubes
We propose to tackle the problem of understanding the effect of regularization in Sinkhorn algotihms.
no code implementations • 26 May 2020 • Eustasio del Barrio, Paula Gordaliza, Jean-Michel Loubes
A review of the main fairness definitions and fair learning methodologies proposed in the literature over the last years is presented from a mathematical point of view.
1 code implementation • 31 Mar 2020 • Philippe Besse, Eustasio del Barrio, Paula Gordaliza, Jean-Michel Loubes, Laurent Risser
Applications based on Machine Learning models have now become an indispensable part of the everyday life and the professional world.
1 code implementation • 18 Jul 2019 • Eustasio del Barrio, Hristo Inouzhe, Jean-Michel Loubes, Carlos Matrán, Agustín Mayo-Íscar
We also present $optimalFlowClassification$, which uses a database of gated cytometries and optimalFlowTemplates to assign cell types to a new cytometry.
1 code implementation • 10 Apr 2019 • Eustasio del Barrio, Hristo Inouzhe, Jean-Michel Loubes
We consider the problem of diversity enhancing clustering, i. e, developing clustering methods which produce clusters that favour diversity with respect to a set of protected attributes such as race, sex, age, etc.
2 code implementations • 17 Jul 2018 • Philippe Besse, Eustasio del Barrio, Paula Gordaliza, Jean-Michel Loubes
We provide the asymptotic distribution of the major indexes used in the statistical literature to quantify disparate treatment in machine learning.
1 code implementation • 8 Jun 2018 • Eustasio del Barrio, Fabrice Gamboa, Paula Gordaliza, Jean-Michel Loubes
\textit{Fairness} is generally studied in a probabilistic framework where it is assumed that there exists a protected variable, whose use as an input of the algorithm may imply discrimination.
Statistics Theory Statistics Theory 62H30, 68T01