  The theory of statistical learning has focused on variational objectives
expressed on functions. In this note, we discuss motivations to write similar
objectives on measures, in particular to discuss out-of-distribution
generalization and weakly-supervised learning. It raises a natural question:
can one cast usual statistical learning results to objectives expressed on
measures? Does the resulting construction lead to new algorithms of practical
interest?
