Cascading Sum-Product Networks using Robustness

Diarmaid Conaty, Cassio Polpo de Campos, Jesus Martinez del Rincon

Research output: Contribution to conferencePaperpeer-review

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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
Publication statusAccepted - 06 Jul 2018
EventInternational Conference on Probabilistic Graphical Models - The Czech Academy of Sciences, Prague, Czech Republic
Duration: 11 Sep 201814 Sep 2018
Conference number: 9th


ConferenceInternational Conference on Probabilistic Graphical Models
Abbreviated titlePGM
Country/TerritoryCzech Republic
Internet address


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