OperatorSHAP trains FastSHAP-style explainers for neural operators via a function-space attribution framework that remains consistent across grid resolutions without retraining.
Approximating the shapley value without marginal contributions.Proceedings of the AAAI Conference on Artificial Intelligence, 38(12):13246–13255
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EASE minimizes first-order mean squared error for probabilistic value estimation by jointly optimizing the sampling law and surrogate function, outperforming prior methods.
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OperatorSHAP: Fast and Accurate Shapley Value Estimation for Neural Operators
OperatorSHAP trains FastSHAP-style explainers for neural operators via a function-space attribution framework that remains consistent across grid resolutions without retraining.
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First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint
EASE minimizes first-order mean squared error for probabilistic value estimation by jointly optimizing the sampling law and surrogate function, outperforming prior methods.