  Positive Unlabeled (PU) learning refers to the task of learning a binary
classifier given a few labeled positive samples, and a set of unlabeled samples
(which could be positive or negative). In this paper, we propose a novel PU
learning framework, that starts by learning a feature space through
pretext-invariant representation learning and then applies pseudo-labeling to
the unlabeled examples, leveraging the concentration property of the
embeddings. Overall, our proposed approach handily outperforms state-of-the-art
PU learning methods across several standard PU benchmark datasets, while not
requiring a-priori knowledge or estimate of class prior. Remarkably, our method
remains effective even when labeled data is scant, where most PU learning
algorithms falter. We also provide simple theoretical analysis motivating our
proposed algorithms and establish generalization guarantee for our approach.
