We introduce Integrated Weak Learning, a principled framework that integrates weak supervision into the training process of machine learning models. Our approach jointly trains the end-model and a label model that aggregates multiple sources of weak supervision. We introduce a label model that can learn to aggregate weak supervision sources differently for different datapoints and takes into consideration the performance of the end-model during training. We show that our approach outperforms existing weak learning techniques across a set of 6 benchmark classification datasets. When both a small amount of labeled data and weak supervision are present the increase in performance is both consistent and large, reliably getting a 2-5 point test F1 score gain over non-integrated methods.
@article{arxiv.2206.09496,
title = {Integrated Weak Learning},
author = {Peter Hayes and Mingtian Zhang and Raza Habib and Jordan Burgess and Emine Yilmaz and David Barber},
journal= {arXiv preprint arXiv:2206.09496},
year = {2022}
}