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Poster
Inferring Generative Model Structure with Static Analysis
Paroma Varma · Bryan He · Payal Bajaj · Nishith Khandwala · Imon Banerjee · Daniel Rubin · Christopher Ré

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #33 #None
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Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline. A popular solution is combining multiple sources of weak supervision using generative models. The structure of these models affects the quality of the training labels, but is difficult to learn without any ground truth labels. We instead rely on weak supervision sources having some structure by virtue of being encoded programmatically. We present Coral, a paradigm that infers generative model structure by statically analyzing the code for these heuristics, thus significantly reducing the amount of data required to learn structure. We prove that Coral's sample complexity scales quasilinearly with the number of heuristics and number of relations identified, improving over the standard sample complexity, which is exponential in n for learning n-th degree relations. Empirically, Coral matches or outperforms traditional structure learning approaches by up to 3.81 F1 points. Using Coral to model dependencies instead of assuming independence results in better performance than a fully supervised model by 3.07 accuracy points when heuristics are used to label radiology data without ground truth labels.

Keywords: Semi-Supervised Learning · Generative Models · Model Selection and Structure Learning

Author Information

Paroma Varma (Stanford University)
Bryan He (Stanford University)
Payal Bajaj (Stanford University)
Nishith Khandwala (Stanford University)
Imon Banerjee (Stanford University)
Daniel Rubin (Stanford University)

Dr. Rubin is a tenured Associate Professor of Biomedical Data Science, Radiology, and Medicine (Biomedical Informatics Research) at Stanford University. His NIH-funded research program focuses on artificial intelligence in medicine and quantitative imaging, integrating imaging with clinical and molecular data, and mining these Big Data to discover imaging phenotypes that can predict disease biology, define disease subtypes, and personalize treatment. Key contributions include discovering quantitative imaging phenotypes in radiology, pathology, and ophthalmology images that identify novel clinical subtypes of disease that help to determine treatments and improve clinical outcomes. He has over 240 peer-reviewed publications and 10 inventions.

Chris Ré (Stanford)

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