# A Coreset Learning Reality Check

arXiv: https://arxiv.org/abs/2301.06163 · HTML: https://mlsanity.com/paper/2301.06163/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 48 references that are in the map.
- First posted on arXiv 15 Jan 2023.

## Builds on (most cited first, 18 of 18)

- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [What is the State of Neural Network Pruning?](https://mlsanity.com/paper/2003.03033/) (2020)
- [A Metric Learning Reality Check](https://mlsanity.com/paper/2003.08505/) (2020)
- [Accounting for Variance in Machine Learning Benchmarks](https://mlsanity.com/paper/2103.03098/) (2021)
- [Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches](https://mlsanity.com/paper/1907.06902/) (2019)
- [Should we really use post-hoc tests based on mean-ranks?](https://mlsanity.com/paper/1505.02288/) (2015)
- [HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO](https://mlsanity.com/paper/2109.06716/) (2021)
- [Coresets for Scalable Bayesian Logistic Regression](https://mlsanity.com/paper/1605.06423/) (2016)
- [A Step Toward Quantifying Independently Reproducible Machine Learning Research](https://mlsanity.com/paper/1909.06674/) (2019)
- [On Coresets for Logistic Regression](https://mlsanity.com/paper/1805.08571/) (2018)
- [New Frameworks for Offline and Streaming Coreset Constructions](https://mlsanity.com/paper/1612.00889/) (2016)
- [Optimal Subsampling for Large Sample Logistic Regression](https://mlsanity.com/paper/1702.01166/) (2017)
- [Coresets for Classification -- Simplified and Strengthened](https://mlsanity.com/paper/2106.04254/) (2021)
- [Research Reproducibility as a Survival Analysis](https://mlsanity.com/paper/2012.09932/) (2020)
- [Optimal Sub-sampling with Influence Functions](https://mlsanity.com/paper/1709.01716/) (2017)
- [Does the Market of Citations Reward Reproducible Work?](https://mlsanity.com/paper/2204.03829/) (2022)
- [A Siren Song of Open Source Reproducibility](https://mlsanity.com/paper/2204.04372/) (2022)
- [On Coresets for Regularized Loss Minimization](https://mlsanity.com/paper/1905.10845/) (2019)

## Built on by (most cited first, 5 of 5)

- [Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Tests](https://mlsanity.com/paper/2310.17867/) (2023)
- [What Do Machine Learning Researchers Mean by "Reproducible"?](https://mlsanity.com/paper/2412.03854/) (2024)
- [High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates](https://mlsanity.com/paper/2407.06346/) (2024)
- [Generalizability of experimental studies](https://mlsanity.com/paper/2406.17374/) (2024)
- [Kernel-based External Validity of Experimental Studies](https://mlsanity.com/paper/2610.13625/) (2026)

## Abstract (arXiv)

Subsampling algorithms are a natural approach to reduce data size before fitting models on massive datasets. In recent years, several works have proposed methods for subsampling rows from a data matrix while maintaining relevant information for classification. While these works are supported by theory and limited experiments, to date there has not been a comprehensive evaluation of these methods. In our work, we directly compare multiple methods for logistic regression drawn from the coreset and optimal subsampling literature and discover inconsistencies in their effectiveness. In many cases, methods do not outperform simple uniform subsampling.