Advances in Learning Bayesian Networks of Bounded Treewidth

Siqi Nie; Denis D. Maua; Cassio Polpo de Campos; Qiang Ji

Advances in Learning Bayesian Networks of Bounded Treewidth

Siqi Nie, Denis D. Maua, Cassio Polpo de Campos, Qiang Ji

Decision Support Systems

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

Abstract

This work presents novel algorithms for learning Bayesian networks of bounded treewidth. Both exact and approximate methods are developed. The exact method combines mixed integer linear programming formulations for structure learning and treewidth computation. The approximate method consists in sampling k-trees (maximal graphs of treewidth k), and subsequently selecting, exactly or approximately, the best structure whose moral graph is a subgraph of that k-tree. The approaches are empirically compared to each other and to state-of-the-art methods on a collection of public data sets with up to 100 variables.

Original language	English
Title of host publication	Advances in Neural Information Processing Systems 27: 28th Annual Conference on Neural Information Processing Systems 2014
Publisher	Curran Associates Inc.
Pages	2285-2293
Number of pages	9
Volume	3
Publication status	Published - Jan 2014

Cite this

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title = "Advances in Learning Bayesian Networks of Bounded Treewidth",

abstract = "This work presents novel algorithms for learning Bayesian networks of bounded treewidth. Both exact and approximate methods are developed. The exact method combines mixed integer linear programming formulations for structure learning and treewidth computation. The approximate method consists in sampling k-trees (maximal graphs of treewidth k), and subsequently selecting, exactly or approximately, the best structure whose moral graph is a subgraph of that k-tree. The approaches are empirically compared to each other and to state-of-the-art methods on a collection of public data sets with up to 100 variables.",

author = "Siqi Nie and Maua, {Denis D.} and {Polpo de Campos}, Cassio and Qiang Ji",

note = "(top 4%, spotlight presentation, double-blind peer reviewed by >3 reviewers)",

year = "2014",

month = jan,

language = "English",

volume = "3",

pages = "2285--2293",

booktitle = "Advances in Neural Information Processing Systems 27: 28th Annual Conference on Neural Information Processing Systems 2014",

publisher = "Curran Associates Inc.",

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Advances in Learning Bayesian Networks of Bounded Treewidth. / Nie, Siqi; Maua, Denis D.; Polpo de Campos, Cassio et al.
Advances in Neural Information Processing Systems 27: 28th Annual Conference on Neural Information Processing Systems 2014. Vol. 3 Curran Associates Inc., 2014. p. 2285-2293.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

TY - CHAP

T1 - Advances in Learning Bayesian Networks of Bounded Treewidth

AU - Nie, Siqi

AU - Maua, Denis D.

AU - Polpo de Campos, Cassio

AU - Ji, Qiang

N1 - (top 4%, spotlight presentation, double-blind peer reviewed by >3 reviewers)

PY - 2014/1

Y1 - 2014/1

N2 - This work presents novel algorithms for learning Bayesian networks of bounded treewidth. Both exact and approximate methods are developed. The exact method combines mixed integer linear programming formulations for structure learning and treewidth computation. The approximate method consists in sampling k-trees (maximal graphs of treewidth k), and subsequently selecting, exactly or approximately, the best structure whose moral graph is a subgraph of that k-tree. The approaches are empirically compared to each other and to state-of-the-art methods on a collection of public data sets with up to 100 variables.

AB - This work presents novel algorithms for learning Bayesian networks of bounded treewidth. Both exact and approximate methods are developed. The exact method combines mixed integer linear programming formulations for structure learning and treewidth computation. The approximate method consists in sampling k-trees (maximal graphs of treewidth k), and subsequently selecting, exactly or approximately, the best structure whose moral graph is a subgraph of that k-tree. The approaches are empirically compared to each other and to state-of-the-art methods on a collection of public data sets with up to 100 variables.

M3 - Chapter

VL - 3

SP - 2285

EP - 2293

BT - Advances in Neural Information Processing Systems 27: 28th Annual Conference on Neural Information Processing Systems 2014

PB - Curran Associates Inc.

ER -

Advances in Learning Bayesian Networks of Bounded Treewidth

Abstract

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