# Improving greedy core-set configurations for active learning with uncertainty-scaled distances

Source: [Improving greedy core-set configurations for active learning with uncertainty-scaled distances](<https://www.sota2.com/research/paper/68fa6bc61e209131cea303d0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:57:05.246Z

Authors: Yuchen Li, Frank Rudzicz

Published: 2022-02-09T03:16:27.000Z

## Abstract

We scale perceived distances of the core-set algorithm by a factor of uncertainty and search for low-confidence configurations, finding significant improvements in sample efficiency across CIFAR10/100 and SVHN image classification, especially in larger acquisition sizes. We show the necessity of our modifications and explain how the improvement is due to a probabilistic quadratic speed-up in the convergence of core-set loss, under assumptions about the relationship of model uncertainty and misclassification.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2202.04251>)

[PDF](<https://arxiv.org/pdf/2202.04251.pdf>)