Eunice Yuh-Jie Chen,
Arthur Choi,
Adnan Darwiche
;
Proceedings of the Eighth International Conference on Probabilistic Graphical Models, PMLR 52:98-109, 2016.
Abstract
The space of Bayesian network structures is forbiddingly large and hence numerous techniques have been developed to prune this search space, but without eliminating the optimal structure. Such techniques are critical for structure learning to scale to larger datasets with more variables. Prior works exploited properties of the MDL score to prune away large regions of the search space that can be safely ignored by optimal structure learning algorithms. In this paper, we propose new techniques for pruning regions of the search space that can be safely ignored by algorithms that enumerate the k-best Bayesian network structures. Empirically, we show that these techniques allow a state-of-the-art structure enumeration algorithm to scale to datasets with significantly more variables.
@InProceedings{pmlr-v52-chen16,
title = {On Pruning with the MDL Score},
author = {Eunice Yuh-Jie Chen and Arthur Choi and Adnan Darwiche},
booktitle = {Proceedings of the Eighth International Conference on Probabilistic Graphical Models},
pages = {98--109},
year = {2016},
editor = {Alessandro Antonucci and Giorgio Corani and Cassio Polpo Campos}},
volume = {52},
series = {Proceedings of Machine Learning Research},
address = {Lugano, Switzerland},
month = {06--09 Sep},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v52/chen16.pdf},
url = {http://proceedings.mlr.press/v52/chen16.html},
abstract = {The space of Bayesian network structures is forbiddingly large and hence numerous techniques have been developed to prune this search space, but without eliminating the optimal structure. Such techniques are critical for structure learning to scale to larger datasets with more variables. Prior works exploited properties of the MDL score to prune away large regions of the search space that can be safely ignored by optimal structure learning algorithms. In this paper, we propose new techniques for pruning regions of the search space that can be safely ignored by algorithms that enumerate the k-best Bayesian network structures. Empirically, we show that these techniques allow a state-of-the-art structure enumeration algorithm to scale to datasets with significantly more variables.}
}
%0 Conference Paper
%T On Pruning with the MDL Score
%A Eunice Yuh-Jie Chen
%A Arthur Choi
%A Adnan Darwiche
%B Proceedings of the Eighth International Conference on Probabilistic Graphical Models
%C Proceedings of Machine Learning Research
%D 2016
%E Alessandro Antonucci
%E Giorgio Corani
%E Cassio Polpo Campos}
%F pmlr-v52-chen16
%I PMLR
%J Proceedings of Machine Learning Research
%P 98--109
%U http://proceedings.mlr.press
%V 52
%W PMLR
%X The space of Bayesian network structures is forbiddingly large and hence numerous techniques have been developed to prune this search space, but without eliminating the optimal structure. Such techniques are critical for structure learning to scale to larger datasets with more variables. Prior works exploited properties of the MDL score to prune away large regions of the search space that can be safely ignored by optimal structure learning algorithms. In this paper, we propose new techniques for pruning regions of the search space that can be safely ignored by algorithms that enumerate the k-best Bayesian network structures. Empirically, we show that these techniques allow a state-of-the-art structure enumeration algorithm to scale to datasets with significantly more variables.
TY - CPAPER
TI - On Pruning with the MDL Score
AU - Eunice Yuh-Jie Chen
AU - Arthur Choi
AU - Adnan Darwiche
BT - Proceedings of the Eighth International Conference on Probabilistic Graphical Models
PY - 2016/08/15
DA - 2016/08/15
ED - Alessandro Antonucci
ED - Giorgio Corani
ED - Cassio Polpo Campos}
ID - pmlr-v52-chen16
PB - PMLR
SP - 98
DP - PMLR
EP - 109
L1 - http://proceedings.mlr.press/v52/chen16.pdf
UR - http://proceedings.mlr.press/v52/chen16.html
AB - The space of Bayesian network structures is forbiddingly large and hence numerous techniques have been developed to prune this search space, but without eliminating the optimal structure. Such techniques are critical for structure learning to scale to larger datasets with more variables. Prior works exploited properties of the MDL score to prune away large regions of the search space that can be safely ignored by optimal structure learning algorithms. In this paper, we propose new techniques for pruning regions of the search space that can be safely ignored by algorithms that enumerate the k-best Bayesian network structures. Empirically, we show that these techniques allow a state-of-the-art structure enumeration algorithm to scale to datasets with significantly more variables.
ER -
Chen, E.Y., Choi, A. & Darwiche, A.. (2016). On Pruning with the MDL Score. Proceedings of the Eighth International Conference on Probabilistic Graphical Models, in PMLR 52:98-109
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