Integrated Weak Learning
This work addresses the challenge of leveraging weak supervision for machine learning practitioners, offering an incremental improvement over existing techniques.
The paper tackles the problem of integrating weak supervision into model training by jointly training an end-model and a label model that adaptively aggregates weak sources, resulting in consistent performance gains of 2-5 F1 points over non-integrated methods on benchmark datasets.
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.