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Given a machine learning (ML) model and a prediction, explanations can be defined as sets of features which are sufficient for the prediction.
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Gorji, N., Rubin, S.: Sufficient reasons for classifier decisions in the presence of domain constraints. In: AAAI (February 2022)
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Ignatiev, A., Izza, Y., Stuckey, P.J., Marques-Silva, J.: Using MaxSAT for efficient explanations of tree ensembles. In: AAAI. pp. 3776–3785 (2022)
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Izza, Y., Ignatiev, A., Marques-Silva, J.: On tackling explanation redundancy in decision trees. J. Artif. Intell. Res. 75
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Marques-Silva, J., Ignatiev, A.: Delivering trustworthy AI through formal XAI. In: AAAI. pp. 12342–12350 (2022)
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