2024-04-18-bickford-smith24a.md

title	software	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
Making Better Use of Unlabelled Data in Bayesian Active Learning	https://github.com/fbickfordsmith/epig	Fully supervised models are predominant in Bayesian active learning. We argue that their neglect of the information present in unlabelled data harms not just predictive performance but also decisions about what data to acquire. Our proposed solution is a simple framework for semi-supervised Bayesian active learning. We find it produces better-performing models than either conventional Bayesian active learning or semi-supervised learning with randomly acquired data. It is also easier to scale up than the conventional approach. As well as supporting a shift towards semi-supervised models, our findings highlight the importance of studying models and acquisition methods in conjunction.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	bickford-smith24a	0	Making Better Use of Unlabelled Data in {B}ayesian Active Learning	847	855	847-855	847	false	Bickford Smith, Freddie and Foster, Adam and Rainforth, Tom	given family Freddie Bickford Smith given family Adam Foster given family Tom Rainforth	2024-04-18		Proceedings of The 27th International Conference on Artificial Intelligence and Statistics	238	inproceedings	date-parts 2024 4 18	https://proceedings.mlr.press/v238/bickford-smith24a/bickford-smith24a.pdf