no code implementations • 12 Mar 2024 • Lucas de Lara, Mathis Deronzier, Alberto González-Sanz, Virgile Foy
The push-forward operation enables one to redistribute a probability measure through a deterministic map.
1 code implementation • 28 Aug 2023 • François Bachoc, Louis Béthune, Alberto González-Sanz, Jean-Michel Loubes
In this paper, we improve the learning theory of kernel distribution regression.
no code implementations • 16 Feb 2022 • Alberto González-Sanz, Lucas de Lara, Louis Béthune, Jean-Michel Loubes
This paper introduces the first statistically consistent estimator of the optimal transport map between two probability distributions, based on neural networks.
1 code implementation • 30 Aug 2021 • Lucas de Lara, Alberto González-Sanz, Nicholas Asher, Laurent Risser, Jean-Michel Loubes
We address the problem of designing realistic and feasible counterfactuals in the absence of a causal model.
1 code implementation • 11 Apr 2021 • Louis Béthune, Thibaut Boissin, Mathieu Serrurier, Franck Mamalet, Corentin Friedrich, Alberto González-Sanz
However they remain commonly considered as less accurate, and their properties in learning are still not fully understood.
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.