2008-07-09-radovilsky08a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_editor	editor	bibtex_author	author	date	note	address	container-title	volume	genre	issued	pdf	extras
Observation subset selection as local compilation of performance profiles	Deciding what to sense is a crucial task, made harder by dependencies and by a non-additive utility function. We develop approximation algorithms for selecting an optimal set of measurements, under a dependency structure modeled by a tree-shaped Bayesian network (BN).Our approach is a generalization of composing anytime algorithm represented by conditional performance profiles. This is done by relaxing the input monotonicity assumption, and extending the local compilation technique to more general classes of performance profiles (PPs). We apply the extended scheme to selecting a subset of measurements for choosing a maximum expectation variable in a binary valued BN, and for minimizing the worst variance in a Gaussian BN.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	radovilsky08a	0	Observation subset selection as local compilation of performance profiles	460	467	460-467	460	false	McAllester, David A. and Myllym{"a}ki, Petri	given family David A. McAllester given family Petri Myllymäki	Radovilsky, Yan and Shimony, Solomon Eyal	given family Yan Radovilsky given family Solomon Eyal Shimony	2008-07-09	Reissued by PMLR on 30 October 2024.		Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence	R6	inproceedings	date-parts 2008 7 9	http://proceedings.mlr.press/r6/radovilsky08a/radovilsky08a.pdf