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

Are labels informative in semi-supervised learning? -- Estimating and leveraging the missing-data mechanism