# Simple Weak Coresets for Non-Decomposable Classification Measures

arXiv: https://arxiv.org/abs/2312.09885 · HTML: https://mlsanity.com/paper/2312.09885/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 12 of 12)

- [Practical Coreset Constructions for Machine Learning](https://mlsanity.com/paper/1703.06476/) (2017)
- [On Coresets for Logistic Regression](https://mlsanity.com/paper/1805.08571/) (2018)
- [Scalable Learning of Non-Decomposable Objectives](https://mlsanity.com/paper/1608.04802/) (2016)
- [New Frameworks for Offline and Streaming Coreset Constructions](https://mlsanity.com/paper/1612.00889/) (2016)
- [Coresets for Classification -- Simplified and Strengthened](https://mlsanity.com/paper/2106.04254/) (2021)
- [sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification](https://mlsanity.com/paper/2108.10566/) (2021)
- [Online and Stochastic Gradient Methods for Non-decomposable Loss Functions](https://mlsanity.com/paper/1410.6776/) (2014)
- [On Coresets for Support Vector Machines](https://mlsanity.com/paper/2002.06469/) (2020)
- [Optimizing Non-decomposable Performance Measures: A Tale of Two Classes](https://mlsanity.com/paper/1505.06812/) (2015)
- [The Power of Uniform Sampling for Coresets](https://mlsanity.com/paper/2209.01901/) (2022)
- [Optimizing Non-decomposable Measures with Deep Networks](https://mlsanity.com/paper/1802.00086/) (2018)
- [Low-Shot Validation: Active Importance Sampling for Estimating Classifier Performance on Rare Categories](https://mlsanity.com/paper/2109.05720/) (2021)

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

- [COREKG: Coreset-Guided Personalized Summarization of Knowledge Graphs](https://mlsanity.com/paper/2605.14900/) (2026)

## Abstract (arXiv)

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.