2023-08-17-cordier23a.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
Flexible and Systematic Uncertainty Estimation with Conformal Prediction via the MAPIE library	Conformal prediction (CP) is an attractive theoretical framework for estimating the uncertainties of any predictive algorithms as its methodology is general and systematic with few assumptions. CP methods can be abstracted into building blocks that can be deployed on any type of data, model, or task. In this work, we contribute to the wide diffusion of the CP framework by developing the library MAPIE1 that implements such principles and can address seamlessly different tasks (e.g. classification, regression, time-series) and in different settings (split and cross-conformal). All these concepts are under a common umbrella with an emphasis on readability, transparency, and reliability, hence supporting the principles of trustworthy AI. An original feature of MAPIE is to offer the possibility of designing tailored-made non-conformity scores in particular p-normalized residual non-conformal scores that can be defined to account for asymmetric errors. We show theoretically the marginal coverage guarantee in several settings. We highlight through applications the interest of choosing different non-conformity scores for tabular data when considering local coverage.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	cordier23a	0	Flexible and Systematic Uncertainty Estimation with Conformal Prediction via the MAPIE library	549	581	549-581	549	false	Cordier, Thibault and Blot, Vincent and Lacombe, Louis and Morzadec, Thomas and Capitaine, Arnaud and Brunel, Nicolas	given family Thibault Cordier given family Vincent Blot given family Louis Lacombe given family Thomas Morzadec given family Arnaud Capitaine given family Nicolas Brunel	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/cordier23a/cordier23a.pdf