# Data Programming using Continuous and Quality-Guided Labeling Functions

arXiv: https://arxiv.org/abs/1911.09860 · HTML: https://mlsanity.com/paper/1911.09860/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2019.

## Built on by (most cited first, 9 of 9)

- [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)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [Knodle: Modular Weakly Supervised Learning with PyTorch](https://mlsanity.com/paper/2104.11557/) (2021)
- [PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training](https://mlsanity.com/paper/2305.13723/) (2023)
- [CHEF: A Cheap and Fast Pipeline for Iteratively Cleaning Label Uncertainties (Technical Report)](https://mlsanity.com/paper/2107.08588/) (2021)
- [WeaNF: Weak Supervision with Normalizing Flows](https://mlsanity.com/paper/2204.13409/) (2022)
- [Regularized Data Programming with Automated Bayesian Prior Selection](https://mlsanity.com/paper/2210.08677/) (2022)
- [INSITE: labelling medical images using submodular functions and semi-supervised data programming](https://mlsanity.com/paper/2402.07173/) (2024)

## Abstract (arXiv)

Scarcity of labeled data is a bottleneck for supervised learning models. A paradigm that has evolved for dealing with this problem is data programming. An existing data programming paradigm allows human supervision to be provided as a set of discrete labeling functions (LF) that output possibly noisy labels to input instances and a generative modelfor consolidating the weak labels. We enhance and generalize this paradigm by supporting functions that output a continuous score (instead of a hard label) that noisily correlates with labels. We show across five applications that continuous LFs are more natural to program and lead to improved recall. We also show that accuracy of existing generative models is unstable with respect to initialization, training epochs, and learning rates. We give control to the data programmer to guide the training process by providing intuitive quality guides with each LF. We propose an elegant method of incorporating these guides into the generative model. Our overall method, called CAGE, makes the data programming paradigm more reliable than other tricks based on initialization, sign-penalties, or soft-accuracy constraints.