Cascading sum-product networks using robustness

Diarmaid Conaty, Jesús Martínez del Rincon, Cassio de Campos

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


Sum-product networks are an increasingly popular family of probabilistic graphical models for which marginal inference can be performed in polynomial time. They have been shown to achieve state-of-the-art performance in several tasks. When learning sum-product networks from scarce data, the obtained model may be prone to robustness issues. In particular, small variations of parameters could lead to different conclusions. We discuss the characteristics of sum-product networks as classifiers and study the robustness of them with respect to their parameters. Using a robustness measure to identify (possibly) unreliable decisions, we build a hierarchical approach where the classification task is deferred to another model if the outcome is deemed unreliable. We apply this approach on benchmark classification tasks and experiments show that the robustness measure can be a meaningful manner to improve classification accuracy.
Original languageEnglish
Title of host publicationProceedings of International Conference on Probabilistic Graphical Models, 11-14 September 2018, Prague, Czech Republic
Number of pages12
Publication statusPublished - 2018
Externally publishedYes
Event9th International Conference on Probabilistic Graphical Models - Prague, Czech Republic
Duration: 11 Sep 201814 Sep 2018

Publication series

NameProceedings of Machine Learning Research
ISSN (Print)1938-7228


Conference9th International Conference on Probabilistic Graphical Models
CountryCzech Republic


  • Sum-product networks
  • sensitivity analysis
  • robustness
  • classification

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