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Assessing heuristic machine learning explanations with model counting

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Publication:2181938
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DOI10.1007/978-3-030-24258-9_19zbMath1441.68211OpenAlexW2953796333MaRDI QIDQ2181938

Alexey Ignatiev, Aditya A. Shrotri, Kuldeep S. Meel, Nina Narodytska, João P. Marques-Silva

Publication date: 20 May 2020

Full work available at URL: https://doi.org/10.1007/978-3-030-24258-9_19



Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)


Related Items (8)

On Tackling Explanation Redundancy in Decision Trees ⋮ \textsf{BDD4BNN}: a BDD-based quantitative analysis framework for binarized neural networks ⋮ \( \mathcal{G} \)-LIME: statistical learning for local interpretations of deep neural networks using global priors ⋮ OuterCount: a first-level solution-counter for quantified Boolean formulas ⋮ On computing probabilistic abductive explanations ⋮ Descriptive accuracy in explanations: the case of probabilistic classifiers ⋮ The Computational Complexity of Understanding Binary Classifier Decisions ⋮ SAT-based rigorous explanations for decision lists




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