optimalFlow: optimal transport approach to flow cytometry gating and population matching

Autor: Eustasio del Barrio, Hristo Inouzhe, Jean-Michel Loubes, Carlos Matrán, Agustín Mayo-Íscar

Publikováno v: BMC Bioinformatics, Vol 21, Iss 1, Pp 1-25 (2020)

Abstract Background Data obtained from flow cytometry present pronounced variability due to biological and technical reasons. Biological variability is a well-known phenomenon produced by measurements on different individuals, with different characte

Externí odkaz: https://doaj.org/article/84a581aa17c8451c85a4ff39a8818b3e

The complex behaviour of Galton rank-order statistic

Autor: Eustasio del Barrio, Juan A. Cuesta-Albertos, Carlos Matran

Publikováno v: Bernoulli, 2022, 28(4), 2123-2150

Galton´s rank-order statistic is one of the oldest statistical tools for two-sample comparisons. It is also a very natural index to measure departures from stochastic dominance. Yet, its asymptotic behaviour has been investigated only partially, und

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1fda4ec75207bad755df1b063c71f3ac
https://doi.org/10.3150/21-bej1406

Attraction-repulsion clustering: a way of promoting diversity linked to demographic parity in fair clustering

Autor: Eustasio del Barrio, Hristo Inouzhe, Jean-Michel Loubes

Producción Científica
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 pro- tected attributes such as race, sex, age, etc. In th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d332e7e9144e1387ce165503ba6c166f
https://doi.org/10.1007/s11634-022-00516-4

Achieving robustness in classification using optimal transport with hinge regularization

Autor: Alberto Gonzalez-Sanz, Thibaut Boissin, Franck Mamalet, Eustasio del Barrio, Jean-Michel Loubes, Mathieu Serrurier

Publikováno v: Conference on Computer Vision and Pattern Recognition
Conference on Computer Vision and Pattern Recognition, Jun 2021, Virtuel, Canada. ⟨10.1109/CVPR46437.2021.00057⟩
Conference on Computer Vision and Pattern Recognition, Jun 2021, Virtuel, Canada
CVPR

Adversarial examples have pointed out Deep Neural Networks vulnerability to small local noise. It has been shown that constraining their Lipschitz constant should enhance robustness, but make them harder to learn with classical loss functions. We pro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f72fe3ab1fd9e6e7aa6f758d8103ba7d
https://hal.science/hal-03033400

A central limit theorem for Lp transportation cost on the real line with application to fairness assessment in machine learning

Autor: Paula Gordaliza, Jean-Michel Loubes, Eustasio del Barrio

Publikováno v: Information and Inference
Information and Inference, Oxford University Press (OUP), 2019, 8 (4), pp.817-849. ⟨10.1093/imaiai/iaz016⟩
Information and Inference, 2019, 8 (4), pp.817-849. ⟨10.1093/imaiai/iaz016⟩

We provide a central limit theorem for the Monge–Kantorovich distance between two empirical distributions with sizes $n$ and $m$, $\mathcal{W}_p(P_n,Q_m), \ p\geqslant 1,$ for observations on the real line. In the case $p>1$ our assumptions are sha

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::75ae7898bb8ebf7e9943e149363c0ec1
https://hal.archives-ouvertes.fr/hal-02963673