# Picking groups instead of samples: A close look at Static Pool-based Meta-Active Learning

arXiv: https://arxiv.org/abs/1911.00314 · HTML: https://mlsanity.com/paper/1911.00314/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2019.

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

- [MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.14339/) (2021)

## Abstract (arXiv)

Active Learning techniques are used to tackle learning problems where obtaining training labels is costly. In this work we use Meta-Active Learning to learn to select a subset of samples from a pool of unsupervised input for further annotation. This scenario is called Static Pool-based Meta- Active Learning. We propose to extend existing approaches by performing the selection in a manner that, unlike previous works, can handle the selection of each sample based on the whole selected subset.