Relevance for robust Bayesian network MAP-explanations

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    Abstract

    In the context of explainable AI, the concept of MAP-independence was recently introduced as a means for conveying the (ir)relevance of intermediate nodes for MAP computations in Bayesian networks. In this paper, we further study the concept of MAP-independence, discuss methods for finding sets of relevant nodes, and suggest ways to use these in providing users with an explanation concerning the robustness of the MAP result.
    Original languageEnglish
    Title of host publicationProceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR
    EditorsAntonio Salmeron, Rafael Rumi
    PublisherMLResearchPress
    Pages13-24
    Publication statusPublished - Oct 2022
    EventInternational Conference on Probabilistic Graphical Models - Almeria, Spain
    Duration: 5 Oct 20227 Oct 2022
    Conference number: 11

    Publication series

    NameProceedings of Machine Learning Research
    Volume186

    Conference

    ConferenceInternational Conference on Probabilistic Graphical Models
    Abbreviated titlePGM
    Country/TerritorySpain
    CityAlmeria
    Period5/10/227/10/22

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