Structured Message Passing

Vibhav Gogate, Pedro Domingos
Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:115-124, 2013.

Abstract

In this paper, we present structured message passing (SMP), a unifying framework for ap- proximate inference algorithms that take advan- tage of structured representations such as al- gebraic decision diagrams and sparse hash ta- bles. These representations can yield signifi- cant time and space savings over the conven- tional tabular representation when the message has several identical values (context-specific in- dependence) or zeros (determinism) or both in its range. Therefore, in order to fully exploit the power of structured representations, we propose to artificially introduce context-specific indepen- dence and determinism in the messages. This yields a new class of powerful approximate in- ference algorithms which includes popular algo- rithms such as cluster-graph Belief propagation (BP), expectation propagation and particle BP as special cases. We show that our new algo- rithms introduce several interesting bias-variance trade-offs. We evaluate these trade-offs empir- ically and demonstrate that our new algorithms are more accurate and scalable than state-of-the- art techniques.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR11-gogate13a, title = {Structured Message Passing}, author = {Gogate, Vibhav and Domingos, Pedro}, booktitle = {Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence}, pages = {115--124}, year = {2013}, editor = {Nicholson, Ann and Smyth, Padhraic}, volume = {R11}, series = {Proceedings of Machine Learning Research}, month = {12--14 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r11/main/assets/gogate13a/gogate13a.pdf}, url = {https://proceedings.mlr.press/r11/gogate13a.html}, abstract = {In this paper, we present structured message passing (SMP), a unifying framework for ap- proximate inference algorithms that take advan- tage of structured representations such as al- gebraic decision diagrams and sparse hash ta- bles. These representations can yield signifi- cant time and space savings over the conven- tional tabular representation when the message has several identical values (context-specific in- dependence) or zeros (determinism) or both in its range. Therefore, in order to fully exploit the power of structured representations, we propose to artificially introduce context-specific indepen- dence and determinism in the messages. This yields a new class of powerful approximate in- ference algorithms which includes popular algo- rithms such as cluster-graph Belief propagation (BP), expectation propagation and particle BP as special cases. We show that our new algo- rithms introduce several interesting bias-variance trade-offs. We evaluate these trade-offs empir- ically and demonstrate that our new algorithms are more accurate and scalable than state-of-the- art techniques.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Structured Message Passing %A Vibhav Gogate %A Pedro Domingos %B Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2013 %E Ann Nicholson %E Padhraic Smyth %F pmlr-vR11-gogate13a %I PMLR %P 115--124 %U https://proceedings.mlr.press/r11/gogate13a.html %V R11 %X In this paper, we present structured message passing (SMP), a unifying framework for ap- proximate inference algorithms that take advan- tage of structured representations such as al- gebraic decision diagrams and sparse hash ta- bles. These representations can yield signifi- cant time and space savings over the conven- tional tabular representation when the message has several identical values (context-specific in- dependence) or zeros (determinism) or both in its range. Therefore, in order to fully exploit the power of structured representations, we propose to artificially introduce context-specific indepen- dence and determinism in the messages. This yields a new class of powerful approximate in- ference algorithms which includes popular algo- rithms such as cluster-graph Belief propagation (BP), expectation propagation and particle BP as special cases. We show that our new algo- rithms introduce several interesting bias-variance trade-offs. We evaluate these trade-offs empir- ically and demonstrate that our new algorithms are more accurate and scalable than state-of-the- art techniques. %Z Reissued by PMLR on 04 October 2026.
APA
Gogate, V. & Domingos, P.. (2013). Structured Message Passing. Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R11:115-124 Available from https://proceedings.mlr.press/r11/gogate13a.html. Reissued by PMLR on 04 October 2026.

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