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20182026
most citedThe Equivalence of Fourier-based and Wasserstein Metrics on Imaging Problems

2 citations · 4 across the 17 of their papers we have counts for

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math.OC2025

On Rank Graduation Metrics for High Dimensional Ordinal Data

Gennaro Auricchio, Adelaide Emma Bernardelli, Paolo Giudici +1

Evaluating the reliability of machine learning classifications remains a fundamental challenge in Artificial Intelligence (AI), particularly when the target variable is multidimens…

math.OC2025

On the computation of the infinity Wasserstein distance and the Wasserstein Projection Problem

Gennaro Auricchio, Gabriele Loli, Marco Veneroni

Computing the infinity Wasserstein distance and retrieving projections of a probability measure onto a closed subset of probability measures are critical sub-problems in various ap…

math.OC2021

On the Pythagorean Structure of the Optimal Transport for Separable Cost Functions

Gennaro Auricchio

In this paper, we study the optimal transport problem induced by separable cost functions. In this framework, transportation can be expressed as the composition of two lower-dimens…

math.OC2021

On the Structure of Optimal Transportation Plans between Discrete Measures

Gennaro Auricchio, Marco Veneroni

In this paper, we prove a structure theorem for discrete optimal transportation plans. We show that, given any pair of discrete probability measures and a cost function, there exis…

math.OC2020★ 2 cited

The Equivalence of Fourier-based and Wasserstein Metrics on Imaging Problems

Gennaro Auricchio, Andrea Codegoni, Stefano Gualandi +2

We investigate properties of some extensions of a class of Fourier-based probability metrics, originally introduced to study convergence to equilibrium for the solution to the spat…

math.OC2018

Computing Kantorovich-Wasserstein Distances on -dimensional histograms using -partite graphs

Gennaro Auricchio, Federico Bassetti, Stefano Gualandi +1

This paper presents a novel method to compute the exact Kantorovich-Wasserstein distance between a pair of -dimensional histograms having bins each. We prove that this probl…