Paweł Teisseyre

Learning from biased positive-unlabeled data via threshold calibration

Learning from positive and unlabeled data (PU learning) aims to train a binary classification model when only positive and unlabeled examples are available. Typically, learners assume that there is a labeling mechanism that determines which positive labels are observed. A particularly challenging s…