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Machine learning approached through supervised learning requires expensiveannotation of data. This motivates weakly supervised learning, where data areannotated with incomplete yet discriminative information. In this paper, wefocus on partial labelling, an instance of weak supervision where, from a giveninput, we are given a set of potential targets. We review a disambiguationprinciple to recover full supervision from weak supervision, and propose anempirical disambiguation algorithm. We prove exponential convergence rates ofour algorithm under classical learnability assumptions, and we illustrate theusefulness of our method on practical examples.

Disambiguation of weak supervision with exponential convergence rates