Focused Belief Propagation for Query-Specific Inference

Anton Chechetka, Carlos Guestrin
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:89-96, 2010.

Abstract

With the increasing popularity of large-scale probabilistic graphical models, even “lightweight” approximate inference methods are becoming infeasible. Fortunately, often large parts of the model are of no immediate interest to the end user. Given the variable that the user actually cares about, we show how to quantify edge importance in graphical models and to significantly speed up inference by focusing computation on important parts of the model. Our algorithm empirically demonstrates convergence speedup by multiple times over state of the art

Cite this Paper

BibTeX
@InProceedings{pmlr-v9-chechetka10a, title = {Focused Belief Propagation for Query-Specific Inference}, author = {Chechetka, Anton and Guestrin, Carlos}, booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics}, pages = {89--96}, year = {2010}, editor = {Teh, Yee Whye and Titterington, Mike}, volume = {9}, series = {Proceedings of Machine Learning Research}, address = {Chia Laguna Resort, Sardinia, Italy}, month = {13--15 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v9/chechetka10a/chechetka10a.pdf}, url = {https://proceedings.mlr.press/v9/chechetka10a.html}, abstract = {With the increasing popularity of large-scale probabilistic graphical models, even “lightweight” approximate inference methods are becoming infeasible. Fortunately, often large parts of the model are of no immediate interest to the end user. Given the variable that the user actually cares about, we show how to quantify edge importance in graphical models and to significantly speed up inference by focusing computation on important parts of the model. Our algorithm empirically demonstrates convergence speedup by multiple times over state of the art} }
Endnote
%0 Conference Paper %T Focused Belief Propagation for Query-Specific Inference %A Anton Chechetka %A Carlos Guestrin %B Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2010 %E Yee Whye Teh %E Mike Titterington %F pmlr-v9-chechetka10a %I PMLR %P 89--96 %U https://proceedings.mlr.press/v9/chechetka10a.html %V 9 %X With the increasing popularity of large-scale probabilistic graphical models, even “lightweight” approximate inference methods are becoming infeasible. Fortunately, often large parts of the model are of no immediate interest to the end user. Given the variable that the user actually cares about, we show how to quantify edge importance in graphical models and to significantly speed up inference by focusing computation on important parts of the model. Our algorithm empirically demonstrates convergence speedup by multiple times over state of the art
RIS
TY - CPAPER TI - Focused Belief Propagation for Query-Specific Inference AU - Anton Chechetka AU - Carlos Guestrin BT - Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics DA - 2010/03/31 ED - Yee Whye Teh ED - Mike Titterington ID - pmlr-v9-chechetka10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 9 SP - 89 EP - 96 L1 - http://proceedings.mlr.press/v9/chechetka10a/chechetka10a.pdf UR - https://proceedings.mlr.press/v9/chechetka10a.html AB - With the increasing popularity of large-scale probabilistic graphical models, even “lightweight” approximate inference methods are becoming infeasible. Fortunately, often large parts of the model are of no immediate interest to the end user. Given the variable that the user actually cares about, we show how to quantify edge importance in graphical models and to significantly speed up inference by focusing computation on important parts of the model. Our algorithm empirically demonstrates convergence speedup by multiple times over state of the art ER -
APA
Chechetka, A. & Guestrin, C.. (2010). Focused Belief Propagation for Query-Specific Inference. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 9:89-96 Available from https://proceedings.mlr.press/v9/chechetka10a.html.

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