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Shapley values has established itself as one of the most appropriate and theoretically sound frameworks for explaining predictions from complex machine learning models.
Aas, K., Jullum, M., and Løland, A. (2019) · 1903
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A Value for N-Person Games
Shapley, L. S. (1953) · 1953
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True to the model or true to the data?
Chen, H., Janizek, J. D., Lundberg, S., and Lee, S.-I. (2020) · 2006
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Shapley-based explainability on the data manifold
Frye, C., de Mijolla, D., Cowton, L., Stanley, M., and Feige, I. (2020) · 2006
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Estimators of relative importance in linear regression based on variance decomposition
Grömping, U. (2007) · 2007
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Axiomatic characterizations of generalized values
Marichal, J.-L., Kojadinovic, I., and Fujimoto, K. (2007) · 2007
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Transversality of the shapley value
Moretti, S. and Patrone, F. (2008) · 2008
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Finding groups in data: an introduction to cluster analysis
Kaufman, L. and Rousseeuw, P. J. (2009) · 2009
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Li, X., Dvornek, N. C., Zhou, Y., Zhuang, J., Ventola, P., and Duncan, J. S. (2019) · 2019
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Feature relevance quantification in explainable ai: A causal problem
Janzing, D., Minorics, L., and Blöbaum, P. (2020) · 2020
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Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020) · 2020
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Interpretable machine learning: A guide for making black box models explainable
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Explaining predictive models with mixed features using shapley values and conditional inference trees
Redelmeier, A., Jullum, M., and Aas, K. (2020) · 2020
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Kvamme, H., Sellereite, N., Aas, K., and Sjursen, S. (2018) · 2018
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Handbook of the Shapley value
Algaba, E., Fragnelli, V., and Sánchez-Soriano, J. (2019) · 2019
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Chen, J., Song, L., Wainwright, M. J., and Jordan, M. I. (2019) · 2019
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Aas, K., Nagler, T., Jullum, M., and Løland, A. (2021) · 2021
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Sellereite, N. and Jullum, M. (2020) · 2027
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