12 citations · 54 across the 12 of their papers we have counts for
12 papers
Improved learning theory for kernel distribution regression with two-stage sampling
François Bachoc, Louis Béthune, Alberto González-Sanz +1
The distribution regression problem encompasses many important statistics and machine learning tasks, and arises in a large range of applications. Among various existing approaches…
Weak Limits for Empirical Entropic Optimal Transport: Beyond Smooth Costs
Alberto González-Sanz, Shayan Hundrieser
We establish weak limits for the empirical entropy regularized optimal transport cost, the expectation of the empirical plan and the conditional expectation. Our results require on…
Gaussian Processes on Distributions based on Regularized Optimal Transport
François Bachoc, Louis Béthune, Alberto Gonzalez-Sanz +1
We present a novel kernel over the space of probability measures based on the dual formulation of optimal regularized transport. We propose an Hilbertian embedding of the space of…
Weak limits of entropy regularized Optimal Transport; potentials, plans and divergences
Alberto Gonzalez-Sanz, Jean-Michel Loubes, Jonathan Niles-Weed
This work deals with the asymptotic distribution of both potentials and couplings of entropic regularized optimal transport for compactly supported probabilities in . We firs…
An improved central limit theorem and fast convergence rates for entropic transportation costs
Eustasio del Barrio, Alberto Gonzalez-Sanz, Jean-Michel Loubes +1
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 allo…
Transport-based Counterfactual Models
Lucas de Lara, Alberto González-Sanz, Nicholas Asher +2
Counterfactual frameworks have grown popular in machine learning for both explaining algorithmic decisions but also defining individual notions of fairness, more intuitive than typ…
Two-sample goodness-of-fit tests on the flat torus based on Wasserstein distance and their relevance to structural biology
Javier González-Delgado, Alberto González-Sanz, Juan Cortés +1
This work is motivated by the study of local protein structure, which is defined by two variable dihedral angles that take values from probability distributions on the flat torus.…
A Central Limit Theorem for Semidiscrete Wasserstein Distances
Eustasio del Barrio, Alberto González-Sanz, Jean-Michel Loubes
We address the problem of proving a Central Limit Theorem for the empirical optimal transport cost, , in the semi discrete case…
Pay attention to your loss: understanding misconceptions about 1-Lipschitz neural networks
Louis Béthune, Thibaut Boissin, Mathieu Serrurier +3
Lipschitz constrained networks have gathered considerable attention in the deep learning community, with usages ranging from Wasserstein distance estimation to the training of cert…
Central Limit Theorems for General Transportation Costs
Eustasio del Barrio, Alberto González-Sanz, Jean-Michel Loubes
We consider the problem of optimal transportation with general cost between a empirical measure and a general target probability on R d , with d 1. We extend results in [19]…
Achieving robustness in classification using optimal transport with hinge regularization
Mathieu Serrurier, Franck Mamalet, Alberto González-Sanz +3
Adversarial examples have pointed out Deep Neural Networks vulnerability to small local noise. It has been shown that constraining their Lipschitz constant should enhance robustnes…
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…