Explaining the data or explaining a model? Shapley values that uncover
non-linear dependencies

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Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies

12 July 2020

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Papers citing "Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies"

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Title
GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanations Rick Wilming Artur Dox Hjalmar Schulz Marta Oliveira Benedict Clark Stefan Haufe 100 2 0 17 Jun 2024
Beyond Cuts in Small Signal Scenarios -- Enhanced Sneutrino Detectability Using Machine Learning Daniel Alvestad N. Fomin Jörn Kersten S. Maeland Inga Strümke 61 11 0 06 Aug 2021
Shapley values for feature selection: The good, the bad, and the axioms D. Fryer Inga Strümke Hien Nguyen FAtt TDI 105 205 0 22 Feb 2021
Towards interpreting ML-based automated malware detection models: a survey Yuzhou Lin Xiaolin Chang 124 7 0 15 Jan 2021