Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision

The paper proposes a statistical label model called FABLE that incorporates instance features to improve the accuracy of inferred truth in Programmatic Weak Supervision (PWS). FABLE is built on a mixture of Bayesian label models, where the coefficients of the mixture components are predicted by a Gaussian Process classifier based on instance features.