  This letter introduces an abstract learning problem called the "set
embedding": The objective is to map sets into probability distributions so as
to lose less information. We relate set union and intersection operations with
corresponding interpolations of probability distributions. We also demonstrate
a preliminary solution with experimental results on toy set embedding examples.
