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2007.06011
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Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies
12 July 2020
D. Fryer
Inga Strümke
Hien Nguyen
TDI
FAtt
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Papers citing
"Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies"
4 / 4 papers shown
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
346
6
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
291
12
0
06 Aug 2021
Shapley values for feature selection: The good, the bad, and the axioms
IEEE Access (IEEE Access), 2021
D. Fryer
Inga Strümke
Hien Nguyen
FAtt
TDI
332
278
0
22 Feb 2021
Towards interpreting ML-based automated malware detection models: a survey
Yuzhou Lin
Xiaolin Chang
342
8
0
15 Jan 2021
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