# Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples

arXiv: https://arxiv.org/abs/2212.10024 · HTML: https://mlsanity.com/paper/2212.10024/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2022.

## Built on by (most cited first, 4 of 4)

- [A Framework for Efficient Model Evaluation through Stratification, Sampling, and Estimation](https://mlsanity.com/paper/2406.07320/) (2024)
- [Evaluation of adaptive sampling methods in scenario generation for virtual safety impact assessment of pre-crash safety systems](https://mlsanity.com/paper/2503.00815/) (2025)
- [Active Data Sampling and Generation for Bias Remediation](https://mlsanity.com/paper/2503.20414/) (2025)
- [Large Language Models, Machine-Assisted Cultural Analytics, and the Agentic Turn](https://mlsanity.com/paper/2610.12792/) (2026)

## Abstract (arXiv)

Data subsampling has become widely recognized as a tool to overcome computational and economic bottlenecks in analyzing massive datasets. We contribute to the development of adaptive design for estimation of finite population characteristics, using active learning and adaptive importance sampling. We propose an active sampling strategy that iterates between estimation and data collection with optimal subsamples, guided by machine learning predictions on yet unseen data. The method is illustrated on virtual simulation-based safety assessment of advanced driver assistance systems. Substantial performance improvements are demonstrated compared to traditional sampling methods.