Observation Subset Selection as Local Compilation of Performance Profiles
Abstract
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.
Cite
Text
Radovilsky and Shimony. "Observation Subset Selection as Local Compilation of Performance Profiles." Conference on Uncertainty in Artificial Intelligence, 2008.Markdown
[Radovilsky and Shimony. "Observation Subset Selection as Local Compilation of Performance Profiles." Conference on Uncertainty in Artificial Intelligence, 2008.](https://mlanthology.org/uai/2008/radovilsky2008uai-observation/)BibTeX
@inproceedings{radovilsky2008uai-observation,
title = {{Observation Subset Selection as Local Compilation of Performance Profiles}},
author = {Radovilsky, Yan and Shimony, Solomon Eyal},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2008},
pages = {460-467},
url = {https://mlanthology.org/uai/2008/radovilsky2008uai-observation/}
}