Publications

On sparsity, power-law and clustering properties of graphex processes Permalink

Published in Published in Advances of Applied Probability, 2023

François Caron, Francesca Panero and Judith Rousseau

This paper investigates properties of the class of graphs based on exchangeable point processes. We talk a lot about sparsity levels, degree distributions (power-law), positive clustering coefficients and central limit theorems. And we show how these properties hold for many many models. See paper here Read more

Achieving Fairness with a Simple Ridge Penalty Permalink

Published in Statistics and Computing, 2022

Marco Scutari, Francesca Panero, Manuel Proissl

In this paper we present a general framework for estimating regression models subject to a user-defined level of fairness. We do that using ridge regression, which is flexible, has already tons of literature and in general simplifies a lot the estimation. See paper here Read more

Bayesian nonparametric disclosure risk assessment Permalink

Published in Electronic Journal of Statistics, 2021

Stefano Favaro, Francesca Panero, Tommaso Rigon

In this paper we propose a Bayesian nonparametric estimator of disclosure risk. We find the posterior distribution of it, which is pretty cool. See paper here Read more

Optimal disclosure risk assessment Permalink

Published in Annals of Statistics, 2021

Federico Camerlenghi, Stefano Favaro, Zacharie Naulet, Francesca Panero

We propose a nonparametric estimator of disclosure risk and prove its minimax optimality. It is nice because we close an open problem in the literature (20+ years!) and the optimality proof is kind of crazy (credits to Zachary). See paper here Read more