2024-09-10-okanik24a.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
Uncertainty Quantification for Metamodels	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/okanik24a/okanik24a.pdf	https://proceedings.mlr.press/v230/okanik24a.html	In the realm of computational science, metamodels serve as indispensable tools for approximating complex systems, facilitating the exploration of scenarios where traditional modelling may prove computationally infeasible. However, the inherent uncertainties within these metamodels, particularly those driven by Machine Learning (ML), necessitate rigorous quantification to ensure reliability and robustness in decision-making processes. One alternative of obtaining uncertainty estimates is using ML models that have a native notion of uncertainty, such as the Bayesian Neural Networks (BNNs), however its repeated sampling necessary to approximate the output distribution is computationally demanding and might defeat the purpose of building metamodels in the first place. In datasets with multidimensional input space and a limited amount of training examples, error estimates provided by BNNs often have poor quality. This study explores alternative empirical approaches to uncertainty quantification, based on knowledge extraction from output space as opposed to input space. Leveraging patterns of magnitude of error committed by the metamodel in output space, we obtain significant improvement of adaptivity of prediction intervals, both over pure Conformal Prediction (CP) and BNNs. Our findings underscore the potential of integrating diverse uncertainty quantification methods to fortify reliability of metamodels, highlighting their robust and quantifiable confidence in model predictions.	inproceedings	2640-3498	okanik24a	Uncertainty Quantification for Metamodels	315	344	315-344	315	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	Ok\'anik, Martin and Trantas, Athanasios and de Bakker, Merijn Pepijn and Lazovik, Elena	given family Martin Okánik given family Athanasios Trantas given family prefix Merijn Pepijn Bakker de given family Elena Lazovik	2024-09-10		Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications	inproceedings	date-parts 2024 9 10