Optimal transport (OT) distances are increasingly used as loss functions for statistical inference, notably in the learning of generative models or supervised learning. For OT ideas to continue to bear fruit in ML, it will be necessary to tackle longstanding challenges, from both statistical and computational points of view: the computation is...
