1 CSAIL, MIT | 2 DataHeroes | 3 Rice University
Alaa Maalouf, Murad Tukan, Vladimir Braverman, and Daniela Rus
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Paper Link: https://arxiv.org/abs/2305.11980
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Explainer video: https://icml.cc/virtual/2023/poster/24432
A coreset is a small weighted subset that approximates the loss function on the whole data, prevalent in machine learning for its advantages. However, current construction methods are problem-dependent and may be challenging for new researchers.
No worries, we got you. We propose AutoCoreset: an automatic practical framework for constructing coresets requiring only input data and the cost function (without any other user computation or calculation), making it user-friendly and applicable to various problems. See our open-source code which supports future research and simplifies coreset usage.
To use AutoCoreset on your data and desired ML model:
(1) Modify Line - 134 to be a list containing the path to your Dataset.
(2) Modify Line - 138 to be the name of your ML model (currently we support 'k_means', 'logistic_regression', 'linear_regression', 'svm').
(3) You can certainly change the ML model as you wish - as long as you provide the "fit" and "score" functions.
(4) Run: python main.py
If you find this work helpful please cite us:
@article{maalouf2023autocoreset,
title={AutoCoreset: An Automatic Practical Coreset Construction Framework},
author={Maalouf, Alaa and Tukan, Murad and Braverman, Vladimir and Rus, Daniela},
journal={arXiv preprint arXiv:2305.11980},
year={2023}
}