  Semi-supervised learning is a powerful technique for leveraging unlabeled
data to improve machine learning models, but it can be affected by the presence
of ``informative'' labels, which occur when some classes are more likely to be
labeled than others. In the missing data literature, such labels are called
missing not at random. In this paper, we propose a novel approach to address
this issue by estimating the missing-data mechanism and using inverse
propensity weighting to debias any SSL algorithm, including those using data
augmentation. We also propose a likelihood ratio test to assess whether or not
labels are indeed informative. Finally, we demonstrate the performance of the
proposed methods on different datasets, in particular on two medical datasets
for which we design pseudo-realistic missing data scenarios.
