Linear and Parallel Learning of Markov Random Fields

Yariv Mizrahi, Misha Denil, Nando De Freitas
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):199-207, 2014.

Abstract

We introduce a new embarrassingly parallel parameter learning algorithm for Markov random fields which is efficient for a large class of practical models. Our algorithm parallelizes naturally over cliques and, for graphs of bounded degree, its complexity is linear in the number of cliques. Unlike its competitors, our algorithm is fully parallel and for log-linear models it is also data efficient, requiring only the local sufficient statistics of the data to estimate parameters.

Cite this Paper

BibTeX
@InProceedings{pmlr-v32-mizrahi14, title = {Linear and Parallel Learning of Markov Random Fields}, author = {Mizrahi, Yariv and Denil, Misha and Freitas, Nando De}, booktitle = {Proceedings of the 31st International Conference on Machine Learning}, pages = {199--207}, year = {2014}, editor = {Xing, Eric P. and Jebara, Tony}, volume = {32}, number = {2}, series = {Proceedings of Machine Learning Research}, address = {Bejing, China}, month = {22--24 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v32/mizrahi14.pdf}, url = {https://proceedings.mlr.press/v32/mizrahi14.html}, abstract = {We introduce a new embarrassingly parallel parameter learning algorithm for Markov random fields which is efficient for a large class of practical models. Our algorithm parallelizes naturally over cliques and, for graphs of bounded degree, its complexity is linear in the number of cliques. Unlike its competitors, our algorithm is fully parallel and for log-linear models it is also data efficient, requiring only the local sufficient statistics of the data to estimate parameters.} }
Endnote
%0 Conference Paper %T Linear and Parallel Learning of Markov Random Fields %A Yariv Mizrahi %A Misha Denil %A Nando De Freitas %B Proceedings of the 31st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2014 %E Eric P. Xing %E Tony Jebara %F pmlr-v32-mizrahi14 %I PMLR %P 199--207 %U https://proceedings.mlr.press/v32/mizrahi14.html %V 32 %N 2 %X We introduce a new embarrassingly parallel parameter learning algorithm for Markov random fields which is efficient for a large class of practical models. Our algorithm parallelizes naturally over cliques and, for graphs of bounded degree, its complexity is linear in the number of cliques. Unlike its competitors, our algorithm is fully parallel and for log-linear models it is also data efficient, requiring only the local sufficient statistics of the data to estimate parameters.
RIS
TY - CPAPER TI - Linear and Parallel Learning of Markov Random Fields AU - Yariv Mizrahi AU - Misha Denil AU - Nando De Freitas BT - Proceedings of the 31st International Conference on Machine Learning DA - 2014/06/18 ED - Eric P. Xing ED - Tony Jebara ID - pmlr-v32-mizrahi14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 32 IS - 2 SP - 199 EP - 207 L1 - http://proceedings.mlr.press/v32/mizrahi14.pdf UR - https://proceedings.mlr.press/v32/mizrahi14.html AB - We introduce a new embarrassingly parallel parameter learning algorithm for Markov random fields which is efficient for a large class of practical models. Our algorithm parallelizes naturally over cliques and, for graphs of bounded degree, its complexity is linear in the number of cliques. Unlike its competitors, our algorithm is fully parallel and for log-linear models it is also data efficient, requiring only the local sufficient statistics of the data to estimate parameters. ER -
APA
Mizrahi, Y., Denil, M. & Freitas, N.D.. (2014). Linear and Parallel Learning of Markov Random Fields. Proceedings of the 31st International Conference on Machine Learning, in Proceedings of Machine Learning Research 32(2):199-207 Available from https://proceedings.mlr.press/v32/mizrahi14.html.

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