Weakly Supervised Learning Creates a Fusion of Modeling Cultures
This is an incremental contribution that discusses potential improvements for machine learning practitioners facing limited labeled data.
The paper addresses the challenge of applying algorithmic modeling under weak supervision, where accurate labels are scarce or expensive, by proposing the integration of data modeling to improve stability and reliability.
The past two decades have witnessed the great success of the algorithmic modeling framework advocated by Breiman et al. (2001). Nevertheless, the excellent prediction performance of these black-box models rely heavily on the availability of strong supervision, i.e. a large set of accurate and exact ground-truth labels. In practice, strong supervision can be unavailable or expensive, which calls for modeling techniques under weak supervision. In this comment, we summarize the key concepts in weakly supervised learning and discuss some recent developments in the field. Using algorithmic modeling alone under a weak supervision might lead to unstable and misleading results. A promising direction would be integrating the data modeling culture into such a framework.