Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product Networks

Denis Deratani Mauá, Heitor Ribeiro Reis, Gustavo Perez Katague, Alessandro Antonucci
Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:293-304, 2020.

Abstract

Sum-product networks are expressive efficient probabilistic graphical models that allow for tractable marginal inference. Many tasks however require the computation of maximum-a-posteriori configurations, an NP-Hard problem for such models. To date there have been very few proposals for computing maximum-a-posteriori configurations in sum-product networks. This is in sharp difference with other probabilistic frameworks such as Bayesian networks and random Markov fields, where the problem is also NP-hard. In this work we propose two approaches to reformulate maximum-a-posteriori inference as other combinatorial optimization problems with widely available solvers. The first approach casts the problem as a similar inference problem in Bayesian networks, overcoming some limitations of previous similar translations. In addition to making available the toolset of maximum-a-posteriori inference on Bayesian networks to sum-product networks, our reformulation also provides further insight into the connections of these two classes of models. The second approach casts the problem as a mixed-integer linear program, for which there exists very efficient solvers. This allows such inferences to be enriched with integer-linear constraints, increasing the expressivity of the models. We compare our reformulation approaches in a large collection of problems, and against state-of-the-art approaches. The results show that reformulation approaches are competitive.

Cite this Paper


BibTeX
@InProceedings{pmlr-v138-maua20a, title = {Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product Networks}, author = {Mau\'a, Denis Deratani and Reis, Heitor Ribeiro and Katague, Gustavo Perez and Antonucci, Alessandro}, booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models}, pages = {293--304}, year = {2020}, editor = {Jaeger, Manfred and Nielsen, Thomas Dyhre}, volume = {138}, series = {Proceedings of Machine Learning Research}, month = {23--25 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v138/maua20a/maua20a.pdf}, url = {http://proceedings.mlr.press/v138/maua20a.html}, abstract = {Sum-product networks are expressive efficient probabilistic graphical models that allow for tractable marginal inference. Many tasks however require the computation of maximum-a-posteriori configurations, an NP-Hard problem for such models. To date there have been very few proposals for computing maximum-a-posteriori configurations in sum-product networks. This is in sharp difference with other probabilistic frameworks such as Bayesian networks and random Markov fields, where the problem is also NP-hard. In this work we propose two approaches to reformulate maximum-a-posteriori inference as other combinatorial optimization problems with widely available solvers. The first approach casts the problem as a similar inference problem in Bayesian networks, overcoming some limitations of previous similar translations. In addition to making available the toolset of maximum-a-posteriori inference on Bayesian networks to sum-product networks, our reformulation also provides further insight into the connections of these two classes of models. The second approach casts the problem as a mixed-integer linear program, for which there exists very efficient solvers. This allows such inferences to be enriched with integer-linear constraints, increasing the expressivity of the models. We compare our reformulation approaches in a large collection of problems, and against state-of-the-art approaches. The results show that reformulation approaches are competitive.} }
Endnote
%0 Conference Paper %T Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product Networks %A Denis Deratani Mauá %A Heitor Ribeiro Reis %A Gustavo Perez Katague %A Alessandro Antonucci %B Proceedings of the 10th International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2020 %E Manfred Jaeger %E Thomas Dyhre Nielsen %F pmlr-v138-maua20a %I PMLR %P 293--304 %U http://proceedings.mlr.press/v138/maua20a.html %V 138 %X Sum-product networks are expressive efficient probabilistic graphical models that allow for tractable marginal inference. Many tasks however require the computation of maximum-a-posteriori configurations, an NP-Hard problem for such models. To date there have been very few proposals for computing maximum-a-posteriori configurations in sum-product networks. This is in sharp difference with other probabilistic frameworks such as Bayesian networks and random Markov fields, where the problem is also NP-hard. In this work we propose two approaches to reformulate maximum-a-posteriori inference as other combinatorial optimization problems with widely available solvers. The first approach casts the problem as a similar inference problem in Bayesian networks, overcoming some limitations of previous similar translations. In addition to making available the toolset of maximum-a-posteriori inference on Bayesian networks to sum-product networks, our reformulation also provides further insight into the connections of these two classes of models. The second approach casts the problem as a mixed-integer linear program, for which there exists very efficient solvers. This allows such inferences to be enriched with integer-linear constraints, increasing the expressivity of the models. We compare our reformulation approaches in a large collection of problems, and against state-of-the-art approaches. The results show that reformulation approaches are competitive.
APA
Mauá, D.D., Reis, H.R., Katague, G.P. & Antonucci, A.. (2020). Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product Networks. Proceedings of the 10th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 138:293-304 Available from http://proceedings.mlr.press/v138/maua20a.html.

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