2024-09-10-balinsky24a.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
Enhancing Conformal Prediction Using E-Test Statistics	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/balinsky24a/balinsky24a.pdf	https://proceedings.mlr.press/v230/balinsky24a.html	Conformal Prediction (CP) serves as a robust framework that quantifies uncertainty in predictions made by Machine Learning (ML) models. Unlike traditional point predictors, CP generates statistically valid prediction regions, also known as prediction intervals, based on the assumption of data exchangeability. Typically, the construction of conformal predictions hinges on p-values. This paper, however, ventures down an alternative path, harnessing the power of e-test statistics to augment the efficacy of conformal predictions by introducing a BB-predictor (bounded from the below predictor). The BB-predictor can be constructed under even more lenient assumptions than exchangeability.	inproceedings	2640-3498	balinsky24a	Enhancing Conformal Prediction Using E-Test Statistics	65	72	65-72	65	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	Balinsky, Alexander A. and Balinsky, Alexander David	given family Alexander A. Balinsky given family Alexander David Balinsky	2024-09-10		Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications	inproceedings	date-parts 2024 9 10