# Inferring Generative Model Structure with Static Analysis

arXiv: https://arxiv.org/abs/1709.02477 · HTML: https://mlsanity.com/paper/1709.02477/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2017.

## Built on by (most cited first, 18 of 18)

- [WRENCH: A Comprehensive Benchmark for Weak Supervision](https://mlsanity.com/paper/2109.11377/) (2021)
- [A Survey on Programmatic Weak Supervision](https://mlsanity.com/paper/2202.05433/) (2022)
- [End-to-End Weak Supervision](https://mlsanity.com/paper/2107.02233/) (2021)
- [DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples](https://mlsanity.com/paper/2110.13740/) (2021)
- [Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming](https://mlsanity.com/paper/2203.01382/) (2022)
- [HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations](https://mlsanity.com/paper/2106.00162/) (2021)
- [Understanding Programmatic Weak Supervision via Source-aware Influence Function](https://mlsanity.com/paper/2205.12879/) (2022)
- [Agile Modeling: From Concept to Classifier in Minutes](https://mlsanity.com/paper/2302.12948/) (2023)
- [Dependency Structure Misspecification in Multi-Source Weak Supervision Models](https://mlsanity.com/paper/2106.10302/) (2021)
- [Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision](https://mlsanity.com/paper/2210.02724/) (2022)
- [Weak Supervision Performance Evaluation via Partial Identification](https://mlsanity.com/paper/2312.04601/) (2023)
- [Label Augmentation with Reinforced Labeling for Weak Supervision](https://mlsanity.com/paper/2204.06436/) (2022)
- [An Adaptive Method for Weak Supervision with Drifting Data](https://mlsanity.com/paper/2306.01658/) (2023)
- [Leveraging Large Language Models for Structure Learning in Prompted Weak Supervision](https://mlsanity.com/paper/2402.01867/) (2024)
- [Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)](https://mlsanity.com/paper/2107.03690/) (2021)
- [Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning](https://mlsanity.com/paper/2107.12591/) (2021)
- [AutoWS: Automated Weak Supervision Framework for Text Classification](https://mlsanity.com/paper/2302.03297/) (2023)
- [DeepStay: Stay Region Extraction from Location Trajectories using Weak Supervision](https://mlsanity.com/paper/2306.06068/) (2023)

## Abstract (arXiv)

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 training label quality, but is difficult to learn without any ground truth labels. We instead rely on these 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 reducing the data required to learn structure significantly. We prove that Coral's sample complexity scales quasilinearly with the number of heuristics and number of relations found, improving over the standard sample complexity, which is exponential in n for identifying n^textrmth degree relations. Experimentally, 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.