SHF: Medium: Program Synthesis for Weak Supervision

Artificial intelligence, in the form of machine learning, has been transformative in automating difficult tasks and extracting insights in numerous problem domains, including language, vision, and recommendations, and offers many further possibilities. For instance, machine learning holds the promise of automatically analyzing medical images, a task previously reserved for human specialists.

However, machine-learning algorithms typically require learning from large amounts of hand-labeled data. Manually labeling data is an expensive and human-intensive process. This project seeks to radically minimize the amount of labeled data needed for machine learning, with the goal of enabling cheap and rapid application of machine learning to new and important domains.

The project leverages program synthesis and weak supervision technology to minimize the amount of labeled data needed to build performant models. Weak supervision replaces hand labels with a number of imprecise sources providing a rough signal for supervised training. Such sources are expressed by labeling functions: small, rough programs that encode knowledge about the task at hand.

The goal of this project is to have labeling functions be generated automatically using program synthesis, eliminating the manual writing of labeling functions and the need for programming expertise. The project develops a generic language of labeling functions, and explores efficient re-use of synthesized functions and richer means of user interaction to further reduce label requirements.

The project is training a diverse group of students. Additionally, the PIs are designing a novel course on machine learning with less labeled data.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.