2024-09-10-lofstrom24b.md

title	booktitle	year	volume	series	month	publisher	pdf	url	abstract	layout	issn	id	tex_title	firstpage	lastpage	page	order	cycles	bibtex_editor	editor	bibtex_author	author	date	address	container-title	genre	issued	extras
Testing Exchangeability between Real and Synthetic Data	Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications	2024	230	Proceedings of Machine Learning Research	0	PMLR	https://raw.githubusercontent.com/mlresearch/v230/main/assets/lofstrom24b/lofstrom24b.pdf	https://proceedings.mlr.press/v230/lofstrom24b.html	This study introduces a method to evaluate synthetic data quality by focusing on the exchangeability of real and synthetic datasets. This is done through the use of a test martingale, which provides a statistical guarantee of the similarity of the synthetic data’s representation of the original data distribution. The method was tested on six real-world datasets and their synthetic counterparts, revealing that traditional metrics such as statistical similarities and model performance may be misleading. The results indicate that the martingale test frequently rejects the hypothesis of data exchangeability, underscore the need for more robust evaluation methods. The martingale-based evaluation offers a straightforward yet effective tool to ensure that synthetic data accurately reflects the original dataset, which is essential for effective model training and validation.	inproceedings	2640-3498	lofstrom24b	Testing Exchangeability between Real and Synthetic Data	424	431	424-431	424	false	Vantini, Simone and Fontana, Matteo and Solari, Aldo and Bostr\"{o}m, Henrik and Carlsson, Lars	given family Simone Vantini given family Matteo Fontana given family Aldo Solari given family Henrik Boström given family Lars Carlsson	L\"ofstr\"om, Helena and Carlsson, Lars and Ahlberg, Ernst	given family Helena Löfström given family Lars Carlsson given family Ernst Ahlberg	2024-09-10		Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications	inproceedings	date-parts 2024 9 10