# Weak Supervision with Incremental Source Accuracy Estimation

arXiv: https://arxiv.org/abs/2205.05302 · HTML: https://mlsanity.com/paper/2205.05302/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 11 May 2022.

## Builds on (most cited first, 3 of 3)

- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Training Complex Models with Multi-Task Weak Supervision](https://mlsanity.com/paper/1810.02840/) (2018)
- [Learning Dependency Structures for Weak Supervision Models](https://mlsanity.com/paper/1903.05844/) (2019)

## Abstract (arXiv)

Motivated by the desire to generate labels for real-time data we develop a method to estimate the dependency structure and accuracy of weak supervision sources incrementally. Our method first estimates the dependency structure associated with the supervision sources and then uses this to iteratively update the estimated source accuracies as new data is received. Using both off-the-shelf classification models trained using publicly-available datasets and heuristic functions as supervision sources we show that our method generates probabilistic labels with an accuracy matching that of existing off-line methods.