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arXiv · 2106.10302

Dependency Structure Misspecification in Multi-Source Weak Supervision Models

Salva Rühling Cachay·Benedikt Boecking·Artur Dubrawski
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Abstract

Data programming (DP) has proven to be an attractive alternative to costly hand-labeling of data. In DP, users encode domain knowledge into \emph{labeling functions} (LF), heuristics that label a subset of the data noisily and may have complex dependencies. A label model is then fit to the LFs to produce an estimate of the unknown class label. The effects of label model misspecification on test set performance of a downstream classifier are understudied. This presents a serious awareness gap to practitioners, in particular since the dependency structure among LFs is frequently ignored in field applications of DP. We analyse modeling errors due to structure over-specification. We derive novel theoretical bounds on the modeling error and empirically show that this error can be substantial, even when modeling a seemingly sensible structure.

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Salva Rühling Cachay, Benedikt Boecking, Artur Dubrawski. 2021-06-18. Dependency Structure Misspecification in Multi-Source Weak Supervision Models. https://arxiv.org/abs/2106.10302

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