SOURCE-LINKED INTELLIGENCE

Learning from Uncertainty-dependent Missing Labels for Semi-supervised Classification

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

Missing labels are usually regarded as a source of information loss in classification. We study a semi-supervised setting in which the probability of label missingness depends on the observed features through posterior classification uncertainty. In this setting, the missingness indicator is not only a record of an unobserved label, but also an observable signal generated by a mechanism linked to the classifier. We develop a likelihood-based information theory for such uncertainty-dependent missing labels. Under correct specification, we derive a Fisher-information decomposition that separates

Read original source ↗ Open in workspace

recordType

paper

region

Global