Markov Network Structure Learning via Ensemble-of-Forests Models

Eirini Arvaniti, Manfred Claassen
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:461-470, 2014.

Abstract

Real world systems typically feature a variety of different dependency types and topologies that complicate model selection for probabilistic graphical models. We introduce the ensemble-of- forests model, a generalization of the ensemble- of-trees model of Meil{ă} and Jaakkola (2006). Our model enables structure learning of Markov random fields (MRF) with multiple connected components and arbitrary potentials. We present two approximate inference techniques for this model and demonstrate their performance on synthetic data. Our results suggest that the ensemble-of-forests approach can accurately re- cover sparse, possibly disconnected MRF topolo- gies, even in presence of non-Gaussian depen- dencies and/or low sample size. We applied the ensemble-of-forests model to learn the struc- ture of perturbed signaling networks of immune cells and found that these frequently exhibit non-Gaussian dependencies with disconnected MRF topologies. In summary, we expect that the ensemble-of-forests model will enable MRF structure learning in other high dimensional real world settings that are governed by non-trivial dependencies.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-arvaniti14a, title = {{M}arkov Network Structure Learning via Ensemble-of-Forests Models}, author = {Arvaniti, Eirini and Claassen, Manfred}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {461--470}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/arvaniti14a/arvaniti14a.pdf}, url = {https://proceedings.mlr.press/r12/arvaniti14a.html}, abstract = {Real world systems typically feature a variety of different dependency types and topologies that complicate model selection for probabilistic graphical models. We introduce the ensemble-of- forests model, a generalization of the ensemble- of-trees model of Meil{ă} and Jaakkola (2006). Our model enables structure learning of Markov random fields (MRF) with multiple connected components and arbitrary potentials. We present two approximate inference techniques for this model and demonstrate their performance on synthetic data. Our results suggest that the ensemble-of-forests approach can accurately re- cover sparse, possibly disconnected MRF topolo- gies, even in presence of non-Gaussian depen- dencies and/or low sample size. We applied the ensemble-of-forests model to learn the struc- ture of perturbed signaling networks of immune cells and found that these frequently exhibit non-Gaussian dependencies with disconnected MRF topologies. In summary, we expect that the ensemble-of-forests model will enable MRF structure learning in other high dimensional real world settings that are governed by non-trivial dependencies.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Markov Network Structure Learning via Ensemble-of-Forests Models %A Eirini Arvaniti %A Manfred Claassen %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-arvaniti14a %I PMLR %P 461--470 %U https://proceedings.mlr.press/r12/arvaniti14a.html %V R12 %X Real world systems typically feature a variety of different dependency types and topologies that complicate model selection for probabilistic graphical models. We introduce the ensemble-of- forests model, a generalization of the ensemble- of-trees model of Meil{ă} and Jaakkola (2006). Our model enables structure learning of Markov random fields (MRF) with multiple connected components and arbitrary potentials. We present two approximate inference techniques for this model and demonstrate their performance on synthetic data. Our results suggest that the ensemble-of-forests approach can accurately re- cover sparse, possibly disconnected MRF topolo- gies, even in presence of non-Gaussian depen- dencies and/or low sample size. We applied the ensemble-of-forests model to learn the struc- ture of perturbed signaling networks of immune cells and found that these frequently exhibit non-Gaussian dependencies with disconnected MRF topologies. In summary, we expect that the ensemble-of-forests model will enable MRF structure learning in other high dimensional real world settings that are governed by non-trivial dependencies. %Z Reissued by PMLR on 04 October 2026.
APA
Arvaniti, E. & Claassen, M.. (2014). Markov Network Structure Learning via Ensemble-of-Forests Models. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:461-470 Available from https://proceedings.mlr.press/r12/arvaniti14a.html. Reissued by PMLR on 04 October 2026.

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