2024-09-12-gedon24a.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
Uncertainty Estimation with Recursive Feature Machines	In conventional regression analysis, predictions are typically represented as point estimates derived from covariates. The Gaussian Process (GP) offer a kernel-based framework that predicts and quantifies associated uncertainties. However, kernel-based methods often underperform ensemble-based decision tree approaches in regression tasks involving tabular and categorical data. Recently, Recursive Feature Machines (RFMs) were proposed as a novel feature-learning kernel which strengthens the capabilities of kernel machines. In this study, we harness the power of these RFMs in a probabilistic GP-based approach to enhance uncertainty estimation through feature extraction within kernel methods. We employ this learned kernel for in-depth uncertainty analysis. On tabular datasets, our RFM-based method surpasses other leading uncertainty estimation techniques, including NGBoost and CatBoost-ensemble. Additionally, when assessing out-of-distribution performance, we found that boosting-based methods are surpassed by our RFM-based approach.	TBKLXswKnO	https://github.com/dgedon/rfm_uncertainty	Papers	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	gedon24a	0	Uncertainty Estimation with Recursive Feature Machines	1408	1437	1408-1437	1408	false	Gedon, Daniel and Abedsoltan, Amirhesam and Sch\"on, Thomas B. and Belkin, Mikhail	given family Daniel Gedon given family Amirhesam Abedsoltan given family Thomas B. Schön given family Mikhail Belkin	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/gedon24a/gedon24a.pdf