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Training Subset Selection for Weak Supervision
Hunter Lang · Aravindan Vijayaraghavan · David Sontag

Tue Nov 29 02:00 PM -- 04:00 PM (PST) @ Hall J #611

Existing weak supervision approaches use all the data covered by weak signals to train a classifier. We show both theoretically and empirically that this is not always optimal. Intuitively, there is a tradeoff between the amount of weakly-labeled data and the precision of the weak labels. We explore this tradeoff by combining pretrained data representations with the cut statistic to select (hopefully) high-quality subsets of the weakly-labeled training data. Subset selection applies to any label model and classifier and is very simple to plug in to existing weak supervision pipelines, requiring just a few lines of code. We show our subset selection method improves the performance of weak supervision for a wide range of label models, classifiers, and datasets. Using less weakly-labeled data improves the accuracy of weak supervision pipelines by up to 19% (absolute) on benchmark tasks.

Author Information

Hunter Lang (MIT)
Aravindan Vijayaraghavan (Northwestern University)
David Sontag (MIT)

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