# Dependency Structure Misspecification in Multi-Source Weak Supervision Models

arXiv: https://arxiv.org/abs/2106.10302 · HTML: https://mlsanity.com/paper/2106.10302/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 18 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting](https://mlsanity.com/paper/1901.09451/) (2019)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [Training Complex Models with Multi-Task Weak Supervision](https://mlsanity.com/paper/1810.02840/) (2018)
- [Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods](https://mlsanity.com/paper/2002.11955/) (2020)
- [Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale](https://mlsanity.com/paper/1812.00417/) (2018)
- [Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling](https://mlsanity.com/paper/2012.06046/) (2020)
- [Learning the Structure of Generative Models without Labeled Data](https://mlsanity.com/paper/1703.00854/) (2017)
- [Learning Dependency Structures for Weak Supervision Models](https://mlsanity.com/paper/1903.05844/) (2019)
- [Inferring Generative Model Structure with Static Analysis](https://mlsanity.com/paper/1709.02477/) (2017)
- [On model misspecification and KL separation for Gaussian graphical models](https://mlsanity.com/paper/1501.02320/) (2015)

## Built on by (most cited first, 6 of 6)

- [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)
- [Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision](https://mlsanity.com/paper/2210.02724/) (2022)
- [Leveraging Large Language Models for Structure Learning in Prompted Weak Supervision](https://mlsanity.com/paper/2402.01867/) (2024)
- [Convergence Behavior of an Adversarial Weak Supervision Method](https://mlsanity.com/paper/2405.16013/) (2024)

## Abstract (arXiv)

Data programming (DP) has proven to be an attractive alternative to costly hand-labeling of data. In DP, users encode domain knowledge into labeling functions (LF), heuristics that label a subset of the data noisily and may have complex dependencies. A label model is then fit to the LFs to produce an estimate of the unknown class label. The effects of label model misspecification on test set performance of a downstream classifier are understudied. This presents a serious awareness gap to practitioners, in particular since the dependency structure among LFs is frequently ignored in field applications of DP. We analyse modeling errors due to structure over-specification. We derive novel theoretical bounds on the modeling error and empirically show that this error can be substantial, even when modeling a seemingly sensible structure.