Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning

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 representation in order to meet certain fairness constraints.