5 citations · 9 across the 21 of their papers we have counts for
4 papers · 1 filter
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…
Energy distance and evolution problems: a promising tool for kinetic equations
Gennaro Auricchio, Giuseppe Toscani
We study the rate of convergence to equilibrium of the solutions to Fokker-Planck type equations with linear drift by means of Cramér and Energy distances, which have been recently…
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…
From Kinetic Theory to AI: a Rediscovery of High-Dimensional Divergences and Their Properties
Gennaro Auricchio, Giovanni Brigati, Paolo Giudici +1
Selecting an appropriate divergence measure is a critical aspect of machine learning, as it directly impacts model performance. Among the most widely used, we find the Kullback-Lei…