June 6, 2024, 4:44 a.m. | Kha-Dinh Luong, Mert Kosan, Arlei Lopes Da Silva, Ambuj Singh

cs.LG updates on arXiv.org arxiv.org

arXiv:2305.15745v2 Announce Type: replace
Abstract: Explaining the decisions made by machine learning models for high-stakes applications is critical for increasing transparency and guiding improvements to these decisions. This is particularly true in the case of models for graphs, where decisions often depend on complex patterns combining rich structural and attribute data. While recent work has focused on designing so-called post-hoc explainers, the broader question of what constitutes a good explanation remains open. One intuitive property is that explanations should be …

abstract applications arxiv case cs.lg cs.si data decisions explainer graph graphs improvements machine machine learning machine learning models optimization patterns replace robust transparency true type while

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