# Minimum-Margin Active Learning

arXiv: https://arxiv.org/abs/1906.00025 · HTML: https://mlsanity.com/paper/1906.00025/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2019.

## Built on by (most cited first, 6 of 6)

- [Chain-of-Thought Reasoning Without Prompting](https://mlsanity.com/paper/2402.10200/) (2024)
- [PixelThink: Towards Efficient Chain-of-Pixel Reasoning](https://mlsanity.com/paper/2505.23727/) (2025)
- [Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack using Public Data](https://mlsanity.com/paper/2302.08466/) (2023)
- [Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active Learning](https://mlsanity.com/paper/2210.15786/) (2022)
- [RD-DPP: Rate-Distortion Theory Meets Determinantal Point Process to Diversify Learning Data Samples](https://mlsanity.com/paper/2304.04137/) (2023)
- [Combining Self-labeling with Selective Sampling](https://mlsanity.com/paper/2301.04420/) (2023)

## Abstract (arXiv)

We present a new active sampling method we call min-margin which trains multiple learners on bootstrap samples and then chooses the examples to label based on the candidates' minimum margin amongst the bootstrapped models. This extends standard margin sampling in a way that increases its diversity in a supervised manner as it arises from the model uncertainty. We focus on the one-shot batch active learning setting, and show theoretically and through extensive experiments on a broad set of problems that min-margin outperforms other methods, particularly as batch size grows.