# Practical Coreset Constructions for Machine Learning

arXiv: https://arxiv.org/abs/1703.06476 · HTML: https://mlsanity.com/paper/1703.06476/

## Key facts

- Cited by 78 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2017.

## Built on by (most cited first, 30 of 78)

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- [Coresets for Classification -- Simplified and Strengthened](https://mlsanity.com/paper/2106.04254/) (2021)
- [A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advances](https://mlsanity.com/paper/2505.17799/) (2025)
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- [Adversarial Robustness of Streaming Algorithms through Importance Sampling](https://mlsanity.com/paper/2106.14952/) (2021)
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- [Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator](https://mlsanity.com/paper/2408.06927/) (2024)
- [Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods](https://mlsanity.com/paper/2302.11962/) (2023)
- [Coresets for Decision Trees of Signals](https://mlsanity.com/paper/2110.03195/) (2021)
- [A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness](https://mlsanity.com/paper/2305.03355/) (2023)
- [Data Summarization via Bilevel Optimization](https://mlsanity.com/paper/2109.12534/) (2021)

## Abstract (arXiv)

We investigate coresets - succinct, small summaries of large data sets - so that solutions found on the summary are provably competitive with solution found on the full data set. We provide an overview over the state-of-the-art in coreset construction for machine learning. In Section 2, we present both the intuition behind and a theoretically sound framework to construct coresets for general problems and apply it to k-means clustering. In Section 3 we summarize existing coreset construction algorithms for a variety of machine learning problems such as maximum likelihood estimation of mixture models, Bayesian non-parametric models, principal component analysis, regression and general empirical risk minimization.