# FAIR: Filtering of Automatically Induced Rules

arXiv: https://arxiv.org/abs/2402.15472 · HTML: https://mlsanity.com/paper/2402.15472/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 23 Feb 2024.

## Builds on (most cited first, 14 of 14)

- [Learning to Reweight Examples for Robust Deep Learning](https://mlsanity.com/paper/1803.09050/) (2018)
- [A critical analysis of self-supervision, or what we can learn from a single image](https://mlsanity.com/paper/1904.13132/) (2019)
- [Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision](https://mlsanity.com/paper/1901.01151/) (2019)
- [Learning from Rules Generalizing Labeled Exemplars](https://mlsanity.com/paper/2004.06025/) (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)
- [PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Subset Selection](https://mlsanity.com/paper/2103.00128/) (2021)
- [Self-Training with Weak Supervision](https://mlsanity.com/paper/2104.05514/) (2021)
- [Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity,Representation, Coverage and Importance](https://mlsanity.com/paper/1901.01153/) (2019)
- [Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming](https://mlsanity.com/paper/2109.11410/) (2021)
- [SPEAR : Semi-supervised Data Programming in Python](https://mlsanity.com/paper/2108.00373/) (2021)
- [Adaptive Mixing of Auxiliary Losses in Supervised Learning](https://mlsanity.com/paper/2202.03250/) (2022)
- [Rule Augmented Unsupervised Constituency Parsing](https://mlsanity.com/paper/2105.10193/) (2021)
- [EIGEN: Expert-Informed Joint Learning Aggregation for High-Fidelity Information Extraction from Document Images](https://mlsanity.com/paper/2311.13993/) (2023)

## Built on by (most cited first, 1 of 1)

- [ARISE: Iterative Rule Induction and Synthetic Data Generation for Text Classification](https://mlsanity.com/paper/2502.05923/) (2025)

## Abstract (arXiv)

The availability of large annotated data can be a critical bottleneck in training machine learning algorithms successfully, especially when applied to diverse domains. Weak supervision offers a promising alternative by accelerating the creation of labeled training data using domain-specific rules. However, it requires users to write a diverse set of high-quality rules to assign labels to the unlabeled data. Automatic Rule Induction (ARI) approaches circumvent this problem by automatically creating rules from features on a small labeled set and filtering a final set of rules from them. In the ARI approach, the crucial step is to filter out a set of a high-quality useful subset of rules from the large set of automatically created rules. In this paper, we propose an algorithm (Filtering of Automatically Induced Rules) to filter rules from a large number of automatically induced rules using submodular objective functions that account for the collective precision, coverage, and conflicts of the rule set. We experiment with three ARI approaches and five text classification datasets to validate the superior performance of our algorithm with respect to several semi-supervised label aggregation approaches. Further, we show that achieves statistically significant results in comparison to existing rule-filtering approaches.