Paula Gordaliza
Paula Gordaliza
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Obtaining fairness using optimal transport theory
P Gordaliza, E Del Barrio, G Fabrice, JM Loubes
International Conference on Machine Learning, 2357-2365, 2019
A central limit theorem for Lp transportation cost on the real line with application to fairness assessment in machine learning
E Del Barrio, P Gordaliza, JM Loubes
Information and Inference: A Journal of the IMA 8 (4), 817-849, 2019
A survey of bias in machine learning through the prism of statistical parity
P Besse, E del Barrio, P Gordaliza, JM Loubes, L Risser
The American Statistician 76 (2), 188-198, 2022
Central limit theorem and bootstrap procedure for Wasserstein’s variations with an application to structural relationships between distributions
E Del Barrio, P Gordaliza, H Lescornel, JM Loubes
Journal of Multivariate Analysis 169, 341-362, 2019
Review of mathematical frameworks for fairness in machine learning
E del Barrio, P Gordaliza, JM Loubes
arXiv preprint arXiv:2005.13755, 2020
Confidence intervals for testing disparate impact in fair learning
P Besse, E del Barrio, P Gordaliza, JM Loubes
arXiv preprint arXiv:1807.06362, 2018
Airports: Análisis de eficiencia operacional basado en trayectorias de vuelo
A Alonso-Isla, PC Álvarez-Esteban, A Bregón, L D’Alto, F Díaz, I García, ...
Actas de las XXIII Jornadas de Ingenieria del Software y Bases de Datos, JISBD, 2018
Fair learning: une approche basée sur le transport optimale
P Gordaliza Pastor
Université de Toulouse, Université Toulouse III-Paul Sabatier, 2020
Fair Learning: an optimal transport based approach
P Gordaliza Pastor
Clasificación no supervisada de datos funcionales: una aplicación a la clasificación con datos de navegación aérea
P Gordaliza Pastor
Baricentros en el espacio de Wasserstein: aplicación a modelos estadísticos de deformación
P Gordaliza Pastor
Application du Transport Optimal en Fair Learning
P Gordaliza, E del Barrio, JM Loubes
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