activity
20172020
most citedReview of Mathematical frameworks for Fairness in Machine Learning

20 citations · 41 across the 4 of their papers we have counts for

collaborators

8 papers

math.ST202010 cited

The statistical effect of entropic regularization in optimal transportation

Eustasio del Barrio, Jean-Michel Loubes

We propose to tackle the problem of understanding the effect of regularization in Sinkhorn algotihms. In the case of Gaussian distributions we provide a closed form for the regular…

stat.ML202020 cited

Review of Mathematical frameworks for Fairness in Machine Learning

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. Following o…

stat.ML20209 cited

A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set

Philippe Besse, Eustasio del Barrio, Paula Gordaliza +2

Applications based on Machine Learning models have now become an indispensable part of the everyday life and the professional world. A critical question then recently arised among…

math.ST20192 cited

A note on the Regularity of Center-Outward Distribution and Quantile Functions

Eustasio del Barrio, Alberto González-Sanz, Marc Hallin

We provide sufficient conditions under which the center-outward distribution and quantile functions introduced in Chernozhukov et al.~(2017) and Hallin~(2017) are homeomorphisms, t…

stat.ML2019

optimalFlow: Optimal-transport approach to flow cytometry gating and population matching

Eustasio del Barrio, Hristo Inouzhe, Jean-Michel Loubes +2

Data obtained from Flow Cytometry present pronounced variability due to biological and technical reasons. Biological variability is a well-known phenomenon produced by measurements…

math.ST2019

On approximate validation of models: A Kolmogorov-Smirnov based approach

Eustasio del Barrio, Hristo Inouzhe, Carlos Matrán

Classical tests of fit typically reject a model for large enough real data samples. In contrast, often in statistical practice a model offers a good description of the data even th…