2024-09-12-damke24a.md

title	abstract	openreview	software	section	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
Linear Opinion Pooling for Uncertainty Quantification on Graphs	We address the problem of uncertainty quantification for graph-structured data, or, more specifically, the problem to quantify the predictive uncertainty in (semi-supervised) node classification. Key questions in this regard concern the distinction between two different types of uncertainty, aleatoric and epistemic, and how to support uncertainty quantification by leveraging the structural information provided by the graph topology. Challenging assumptions and postulates of state-of-the-art methods, we propose a novel approach that represents (epistemic) uncertainty in terms of mixtures of Dirichlet distributions and refers to the established principle of linear opinion pooling for propagating information between neighbored nodes in the graph. The effectiveness of this approach is demonstrated in a series of experiments on a variety of graph-structured datasets.	qLGkfpXTSn	https://github.com/Cortys/gpn-extensions	Papers	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	damke24a	0	Linear Opinion Pooling for Uncertainty Quantification on Graphs	919	929	919-929	919	false	Damke, Clemens and H\"ullermeier, Eyke	given family Clemens Damke given family Eyke Hüllermeier	2024-09-12		Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence	244	inproceedings	date-parts 2024 9 12	https://raw.githubusercontent.com/mlresearch/v244/main/assets/damke24a/damke24a.pdf