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Found 3 papers, 1 papers with code
Are labels informative in semi-supervised learning? -- Estimating and leveraging the missing-data mechanism
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.
Don't fear the unlabelled: safe semi-supervised learning via simple debiasing
Our debiasing approach is straightforward to implement and applicable to most deep SSL methods.
Model-agnostic out-of-distribution detection using combined statistical tests
These techniques, based on classical statistical tests, are model-agnostic in the sense that they can be applied to any differentiable generative model.
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