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stat.ML2026
Learnability with Partial Labels and Adaptive Nearest Neighbors
Nicolas A. Errandonea, Santiago Mazuelas, Jose A. Lozano +1
Prior work on partial labels learning (PLL) has shown that learning is possible even when each instance is associated with a bag of labels, rather than a single accurate but costly…
stat.ML2025
Reliable Programmatic Weak Supervision with Confidence Intervals for Label Probabilities
Verónica Ãlvarez, Santiago Mazuelas, Steven An +1
The accurate labeling of datasets is often both costly and time-consuming. Given an unlabeled dataset, programmatic weak supervision obtains probabilistic predictions for the label…