Remarkably, we find that this causality condition provides a natural framework to parameterize the cost function that is learned by the discriminator as arobust (worst-case) distance, and anideal mechanism for learning time dependent data distributions.

Pre-training has achieved remarkable success when transferred to downstream tasks. In machine learning, we care about not only the good performance of a model but also its behavior under reasonable shifts of condition.

Boosting is a widely used learning technique in machine learning for solving classificationproblems.

Error bars are not conventionally reported in these settings.

Inmicrobiome studies, we may wish to investigate the association between the overall composition of human microbiota, including hundreds of microbial taxa, and multiple host metabolites from aparticular metabolic pathway [3, 4].

They generalize classic feedforward models and are equivalent toinfinite-depth weight-tied feedforwardnetworks.