Zobrazeno 1 - 10
of 112
pro vyhledávání: '"Del Barrio, Eustasio"'
For a probability P in $R^d$ its center outward distribution function $F_{\pm}$, introduced in Chernozhukov et al. (2017) and Hallin et al. (2021), is a new and successful concept of multivariate distribution function based on mass transportation the
Externí odkaz:
http://arxiv.org/abs/2303.16862
Publikováno v:
Advanced Nonlinear Studies, Vol 24, Iss 4, Pp 880-894 (2024)
For a probability P in Rd ${\mathbb{R}}^{d}$ its center outward distribution function F ±, introduced in V. Chernozhukov, A. Galichon, M. Hallin, and M. Henry (“Monge–Kantorovich depth, quantiles, ranks and signs,” Ann. Stat., vol. 45, no. 1,
Externí odkaz:
https://doaj.org/article/c1c19dd0dc5d40bcb8b20845192ed9df
Based on the novel concept of multivariate center-outward quantiles introduced recently in Chernozhukov et al. (2017) and Hallin et al. (2021), we are considering the problem of nonparametric multiple-output quantile regression. Our approach defines
Externí odkaz:
http://arxiv.org/abs/2204.11756
We prove a central limit theorem for the entropic transportation cost between subgaussian probability measures, centered at the population cost. This is the first result which allows for asymptotically valid inference for entropic optimal transport b
Externí odkaz:
http://arxiv.org/abs/2204.09105
We prove a Central Limit Theorem for the empirical optimal transport cost, $\sqrt{\frac{nm}{n+m}}\{\mathcal{T}_c(P_n,Q_m)-\mathcal{T}_c(P,Q)\}$, in the semi discrete case, i.e when the distribution $P$ is supported in $N$ points, but without assumpti
Externí odkaz:
http://arxiv.org/abs/2202.06380
We address the problem of proving a Central Limit Theorem for the empirical optimal transport cost, $\sqrt{n}\{\mathcal{T}_c(P_n,Q)-\mathcal{W}_c(P,Q)\}$, in the semi discrete case, i.e when the distribution $P$ is finitely supported. We show that th
Externí odkaz:
http://arxiv.org/abs/2105.11721
We consider the problem of optimal transportation with general cost between a empirical measure and a general target probability on R d , with d $\ge$ 1. We extend results in [19] and prove asymptotic stability of both optimal transport maps and pote
Externí odkaz:
http://arxiv.org/abs/2102.06379
Autor:
Serrurier, Mathieu, Mamalet, Franck, González-Sanz, Alberto, Boissin, Thibaut, Loubes, Jean-Michel, del Barrio, Eustasio
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:
http://arxiv.org/abs/2006.06520
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 regularized optimal transport which enables to provide a better understanding
Externí odkaz:
http://arxiv.org/abs/2006.05199
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 our independence-based approach, we consider how to build fair algorith
Externí odkaz:
http://arxiv.org/abs/2005.13755