Integrated Weak Learning

• URL: http://arxiv.org/abs/2206.09496v1
• Date: Sun, 19 Jun 2022 22:13:59 GMT
• Title: Integrated Weak Learning
• Authors: Peter Hayes, Mingtian Zhang, Raza Habib, Jordan Burgess, Emine Yilmaz and David Barber
• Abstract summary: Integrated Weak Learning is a principled framework that integrates weak supervision into the training process of machine learning models. We show that our approach outperforms existing weak learning techniques across a set of 6 benchmark classification datasets.
• Score: 25.47289093245517
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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