In contrast, we use 10% of the training set9 for validation, and treat the validation set as apurely held-out test set (this also means that we train on less data).10 Wewillexplainthismoreclearly.30 both spheres are sufficiently tiny (i.e.

Optimal Transport (OT) + Fairness (R2, R4): Let us highlight two key differences between "Wasserstein Fair10 Classification" (Jiang et al.) and our work. Generally group fairness constraints31 are trying to reflect a certain independence between the prediction and the sensitive attribute.

ERM combines two well-understood techniques: mini-batch sampling and gradient-based optimization using backpropagation.

There are two more things to verify.

TRPO, PPO), and a critic that involves minimizing a closely connected objective.