2023-08-17-kato23a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
A Review of Nonconformity Measures for Conformal Prediction in Regression	Conformal prediction provides distribution-free uncertainty quantification under minimal assumptions. An important ingredient in conformal prediction is the so-called nonconformity measure, which quantifies how the test sample differs from the rest of the data. In this paper, existing nonconformity measures from the current literature are collected and their underlying ideas are analyzed. Furthermore, the influence of different factors on the performance of conformal prediction are pointed out by focusing on the relation between the influencing factors and the choice of nonconformity measures. Lastly, we provide suggestions for future work with regard to currently existing knowledge gaps and development of new nonconformity measures.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	kato23a	0	A Review of Nonconformity Measures for Conformal Prediction in Regression	369	383	369-383	369	false	Kato, Yuko and Tax, David M.J. and Loog, Marco	given family Yuko Kato given family David M.J. Tax given family Marco Loog	2023-08-17		Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications	204	inproceedings	date-parts 2023 8 17	https://proceedings.mlr.press/v204/kato23a/kato23a.pdf