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Creators/Authors contains: "Covert, Ian"

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  1. ; ; ; ; (, ICLR 2022)
    Although Shapley values are theoretically appealing for explaining black-box models, they are costly to calculate and thus impractical in settings that involve large, high-dimensional models. To remedy this issue, we introduce FastSHAP, a new method for estimating Shapley values in a single forward pass using a learned explainer model. To enable efficient training without requiring ground truth Shapley values, we develop an approach to train FastSHAP via stochastic gradient descent using a weighted least squares objective function. In our experiments with tabular and image datasets, we compare FastSHAP to existing estimation approaches and f ind that it generates accurate explanations with an orders-of-magnitude speedup. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; ; (, IEEE TPAMI)
    Accepted Manuscript Full Text Available