  In this paper we develop a principled, probabilistic, unified approach to
non-standard classification tasks, such as semi-supervised,
positive-unlabelled, multi-positive-unlabelled and noisy-label learning. We
train a classifier on the given labels to predict the label-distribution. We
then infer the underlying class-distributions by variationally optimizing a
model of label-class transitions.
