Solving Multiple Inference by Minimizing Expected Loss

Cong Chen, Jiaqi Yang, Chao Chen, Changhe Yuan
Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:65-76, 2020.

Abstract

Multiple Inference is the problem of finding multiple top solutions for an inference problem in a graphical model. It has been shown that it is beneficial for the top solutions to be diverse. However, existing methods, such as diverse M-Best and M-Modes, often rely on a hyper parameter in enforcing diversity. The optimal values of such parameters usually depend on the probability landscape of the graphical model and thus have to be tuned case by case via cross validation. This is not a desirable property. In this paper, we introduce a parameter-free method that directly minimizes the expected loss of each solution in finding multiple top solutions that have high oracle accuracy, and are automatically diverse. Empirical evaluations show that our method often have better performance than other competing methods.

Cite this Paper


BibTeX
@InProceedings{pmlr-v138-chen20a, title = {Solving Multiple Inference by Minimizing Expected Loss}, author = {Chen, Cong and Yang, Jiaqi and Chen, Chao and Yuan, Changhe}, booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models}, pages = {65--76}, year = {2020}, editor = {Manfred Jaeger and Thomas Dyhre Nielsen}, volume = {138}, series = {Proceedings of Machine Learning Research}, month = {23--25 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v138/chen20a/chen20a.pdf}, url = { http://proceedings.mlr.press/v138/chen20a.html }, abstract = {Multiple Inference is the problem of finding multiple top solutions for an inference problem in a graphical model. It has been shown that it is beneficial for the top solutions to be diverse. However, existing methods, such as diverse M-Best and M-Modes, often rely on a hyper parameter in enforcing diversity. The optimal values of such parameters usually depend on the probability landscape of the graphical model and thus have to be tuned case by case via cross validation. This is not a desirable property. In this paper, we introduce a parameter-free method that directly minimizes the expected loss of each solution in finding multiple top solutions that have high oracle accuracy, and are automatically diverse. Empirical evaluations show that our method often have better performance than other competing methods.} }
Endnote
%0 Conference Paper %T Solving Multiple Inference by Minimizing Expected Loss %A Cong Chen %A Jiaqi Yang %A Chao Chen %A Changhe Yuan %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-chen20a %I PMLR %P 65--76 %U http://proceedings.mlr.press/v138/chen20a.html %V 138 %X Multiple Inference is the problem of finding multiple top solutions for an inference problem in a graphical model. It has been shown that it is beneficial for the top solutions to be diverse. However, existing methods, such as diverse M-Best and M-Modes, often rely on a hyper parameter in enforcing diversity. The optimal values of such parameters usually depend on the probability landscape of the graphical model and thus have to be tuned case by case via cross validation. This is not a desirable property. In this paper, we introduce a parameter-free method that directly minimizes the expected loss of each solution in finding multiple top solutions that have high oracle accuracy, and are automatically diverse. Empirical evaluations show that our method often have better performance than other competing methods.
APA
Chen, C., Yang, J., Chen, C. & Yuan, C.. (2020). Solving Multiple Inference by Minimizing Expected Loss. Proceedings of the 10th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 138:65-76 Available from http://proceedings.mlr.press/v138/chen20a.html .

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