  Machine learning approached through supervised learning requires expensive
annotation of data. This motivates weakly supervised learning, where data are
annotated with incomplete yet discriminative information. In this paper, we
focus on partial labelling, an instance of weak supervision where, from a given
input, we are given a set of potential targets. We review a disambiguation
principle to recover full supervision from weak supervision, and propose an
empirical disambiguation algorithm. We prove exponential convergence rates of
our algorithm under classical learnability assumptions, and we illustrate the
usefulness of our method on practical examples.
