Learning Dependency Structures for Weak Supervision Models

14 Mar 2019Paroma VarmaFrederic SalaAnn HeAlexander RatnerChristopher Ré

Labeling training data is a key bottleneck in the modern machine learning pipeline. Recent weak supervision approaches combine labels from multiple noisy sources by estimating their accuracies without access to ground truth labels; however, estimating the dependencies among these sources is a critical challenge... (read more)

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