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 language | English |
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Title of host publication | Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR |
Editors | Antonio Salmeron, Rafael Rumi |
Publisher | MLResearchPress |
Pages | 13-24 |
Publication status | Published - Oct 2022 |
Event | International Conference on Probabilistic Graphical Models - Almeria, Spain Duration: 5 Oct 2022 → 7 Oct 2022 Conference number: 11 |
Publication series
Name | Proceedings of Machine Learning Research |
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Volume | 186 |
Conference
Conference | International Conference on Probabilistic Graphical Models |
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Abbreviated title | PGM |
Country/Territory | Spain |
City | Almeria |
Period | 5/10/22 → 7/10/22 |