While coresets have been growing in terms of their application, barring few exceptions, they have mostly been limited to unsupervised settings. We consider supervised classification problems, and non-decomposable evaluation measures in such settings. We show that stratified uniform sampling based coresets have excellent empirical performance that are backed by theoretical guarantees too. We focus on the F1 score and Matthews Correlation Coefficient, two widely used non-decomposable objective functions that are nontrivial to optimize for and show that uniform coresets attain a lower bound for coreset size, and have good empirical performance, comparable with ``smarter'' coreset construction strategies.

翻译：尽管核心集在应用方面不断增长，但除少数例外情况外，它们大多局限于无监督场景。我们考虑监督分类问题及其中的非分解评估度量。研究表明，基于分层均匀抽样的核心集不仅在理论上具有保证，而且在实证中表现优异。我们聚焦F1分数和马修斯相关系数这两种广泛使用且难以优化的非分解目标函数，证明均匀核心集能够达到核心集规模的下界，并在实证性能上与“更智能”的核心集构建策略相当。