639 citations · 2.9k across the 94 of their papers we have counts for
3 papers · 2 filters
Learning Fair and Transferable Representations
Luca Oneto, Michele Donini, Andreas Maurer +1
Developing learning methods which do not discriminate subgroups in the population is a central goal of algorithmic fairness. One way to reach this goal is by modifying the data rep…
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm
Giulia Luise, Saverio Salzo, Massimiliano Pontil +1
We present a novel algorithm to estimate the barycenter of arbitrary probability distributions with respect to the Sinkhorn divergence. Based on a Frank-Wolfe optimization strategy…
General Fair Empirical Risk Minimization
Luca Oneto, Michele Donini, Massimiliano Pontil
We tackle the problem of algorithmic fairness, where the goal is to avoid the unfairly influence of sensitive information, in the general context of regression with possible contin…