# Active Learning with Combinatorial Coverage

arXiv: https://arxiv.org/abs/2302.14567 · HTML: https://mlsanity.com/paper/2302.14567/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 10 of 10)

- [DeepXplore: Automated Whitebox Testing of Deep Learning Systems](https://mlsanity.com/paper/1705.06640/) (2017)
- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems](https://mlsanity.com/paper/1803.07519/) (2018)
- [Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation](https://mlsanity.com/paper/1706.04737/) (2017)
- [Contextual Diversity for Active Learning](https://mlsanity.com/paper/2008.05723/) (2020)
- [Influence Selection for Active Learning](https://mlsanity.com/paper/2108.09331/) (2021)
- [Practical Obstacles to Deploying Active Learning](https://mlsanity.com/paper/1807.04801/) (2018)
- [Systematic Training and Testing for Machine Learning Using Combinatorial Interaction Testing](https://mlsanity.com/paper/2201.12428/) (2022)
- [Mitigating Sampling Bias and Improving Robustness in Active Learning](https://mlsanity.com/paper/2109.06321/) (2021)
- [Practical Active Learning with Model Selection for Small Data](https://mlsanity.com/paper/2112.11572/) (2021)

## Built on by (most cited first, 3 of 3)

- [Test & Evaluation Best Practices for Machine Learning-Enabled Systems](https://mlsanity.com/paper/2310.06800/) (2023)
- [Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement](https://mlsanity.com/paper/2504.16136/) (2025)
- [Which LLM to pick? Online Active Model Selection for Large Language Models](https://mlsanity.com/paper/2610.01592/) (2026)

## Abstract (arXiv)

Active learning is a practical field of machine learning that automates the process of selecting which data to label. Current methods are effective in reducing the burden of data labeling but are heavily model-reliant. This has led to the inability of sampled data to be transferred to new models as well as issues with sampling bias. Both issues are of crucial concern in machine learning deployment. We propose active learning methods utilizing combinatorial coverage to overcome these issues. The proposed methods are data-centric, as opposed to model-centric, and through our experiments we show that the inclusion of coverage in active learning leads to sampling data that tends to be the best in transferring to better performing models and has a competitive sampling bias compared to benchmark methods.