# AutoCoreset: An Automatic Practical Coreset Construction Framework

arXiv: https://arxiv.org/abs/2305.11980 · HTML: https://mlsanity.com/paper/2305.11980/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 53 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 23 of 23)

- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [BERT Loses Patience: Fast and Robust Inference with Early Exit](https://mlsanity.com/paper/2006.04152/) (2020)
- [On Coresets for Logistic Regression](https://mlsanity.com/paper/1805.08571/) (2018)
- [Coresets and Sketches](https://mlsanity.com/paper/1601.00617/) (2016)
- [Coresets for Near-Convex Functions](https://mlsanity.com/paper/2006.05482/) (2020)
- [Discrepancy, Coresets, and Sketches in Machine Learning](https://mlsanity.com/paper/1906.04845/) (2019)
- [Distributed k-Means and k-Median Clustering on General Topologies](https://mlsanity.com/paper/1306.0604/) (2013)
- [Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions](https://mlsanity.com/paper/2209.08554/) (2022)
- [On Coresets for Support Vector Machines](https://mlsanity.com/paper/2002.06469/) (2020)
- [Sets Clustering](https://mlsanity.com/paper/2003.04135/) (2020)
- [Introduction to Coresets: Accurate Coresets](https://mlsanity.com/paper/1910.08707/) (2019)
- [New Coresets for Projective Clustering and Applications](https://mlsanity.com/paper/2203.04370/) (2022)
- [One-Shot Coresets: The Case of k-Clustering](https://mlsanity.com/paper/1711.09649/) (2017)
- [Provable Data Subset Selection For Efficient Neural Network Training](https://mlsanity.com/paper/2303.05151/) (2023)
- [Tight Sensitivity Bounds For Smaller Coresets](https://mlsanity.com/paper/1907.01433/) (2019)
- [Fast and Accurate Least-Mean-Squares Solvers](https://mlsanity.com/paper/1906.04705/) (2019)
- [Coresets for Ordered Weighted Clustering](https://mlsanity.com/paper/1903.04351/) (2019)
- [On Coresets for Regularized Loss Minimization](https://mlsanity.com/paper/1905.10845/) (2019)
- [Coresets for Data Discretization and Sine Wave Fitting](https://mlsanity.com/paper/2203.03009/) (2022)
- [Obstacle Aware Sampling for Path Planning](https://mlsanity.com/paper/2203.04075/) (2022)
- [An Efficient Drifters Deployment Strategy to Evaluate Water Current Velocity Fields](https://mlsanity.com/paper/2301.04216/) (2023)
- [Introduction to Coresets: Approximated Mean](https://mlsanity.com/paper/2111.03046/) (2021)
- [Coresets for Vector Summarization with Applications to Network Graphs](https://mlsanity.com/paper/1706.05554/) (2017)

## Built on by (most cited first, 4 of 4)

- [Dataset Distillation Meets Provable Subset Selection](https://mlsanity.com/paper/2307.08086/) (2023)
- [ORBSLAM3-Enhanced Autonomous Toy Drones: Pioneering Indoor Exploration](https://mlsanity.com/paper/2312.13385/) (2023)
- [Efficient Test-Time Finetuning of LLMs via Convex Reconstruction and Gradient Caching](https://mlsanity.com/paper/2605.30337/) (2026)
- [Provable Pruning for Efficient 3D Gaussian Splatting via Coresets](https://mlsanity.com/paper/2607.02721/) (2026)

## Abstract (arXiv)

A coreset is a tiny weighted subset of an input set, that closely resembles the loss function, with respect to a certain set of queries. Coresets became prevalent in machine learning as they have shown to be advantageous for many applications. While coreset research is an active research area, unfortunately, coresets are constructed in a problem-dependent manner, where for each problem, a new coreset construction algorithm is usually suggested, a process that may take time or may be hard for new researchers in the field. Even the generic frameworks require additional (problem-dependent) computations or proofs to be done by the user. Besides, many problems do not have (provable) small coresets, limiting their applicability. To this end, we suggest an automatic practical framework for constructing coresets, which requires (only) the input data and the desired cost function from the user, without the need for any other task-related computation to be done by the user. To do so, we reduce the problem of approximating a loss function to an instance of vector summation approximation, where the vectors we aim to sum are loss vectors of a specific subset of the queries, such that we aim to approximate the image of the function on this subset. We show that while this set is limited, the coreset is quite general. An extensive experimental study on various machine learning applications is also conducted. Finally, we provide a ``plug and play" style implementation, proposing a user-friendly system that can be easily used to apply coresets for many problems. Full open source code can be found at hrefhttps://github.com/alaamaalouf/AutoCoresethttps://github.com/alaamaalouf/AutoCoreset. We believe that these contributions enable future research and easier use and applications of coresets.