NeurIPS 2020

Provably Consistent Partial-Label Learning

Meta Review

This paper presents a method for learning with a set of candidate labels, under the assumption that all label sets that contain the true label are equally likely. This is an odd assumption, but results on 5 partial label learning datasets (that do not necessarily satisfy this assumption) look promising. The paper is detailed and the empirical results compare to 6 other methods.