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• W2912349960 abstract "Current approaches for explaining machine learning models fall into two distinct classes: antecedent event influence and value attribution. The former leverages training instances to describe how much influence a training point exerts on a test point, while the latter attempts to attribute value to the features most pertinent to a given prediction. In this work, we discuss an algorithm, AVA: Aggregate Valuation of Antecedents, that fuses these two explanation classes to form a new approach to feature attribution that not only retrieves local explanations but also captures global patterns learned by a model. Our experimentation convincingly favors weighting and aggregating feature attributions via AVA." @default.
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• W2912349960 date "2019-01-20" @default.
• W2912349960 modified "2023-09-24" @default.
• W2912349960 title "Towards Aggregating Weighted Feature Attributions" @default.
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