Dependency Structure Misspecification in Multi-Source Weak Supervision Models
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.
Cite
@article{arxiv.2106.10302,
title = {Dependency Structure Misspecification in Multi-Source Weak Supervision Models},
author = {Salva Rühling Cachay and Benedikt Boecking and Artur Dubrawski},
journal= {arXiv preprint arXiv:2106.10302},
year = {2021}
}
Comments
Oral presentation at the Workshop on Weakly Supervised Learning at ICLR 2021