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A variety of recent papers discuss the application of Shapley values, a concept for explaining coalitional games, for feature attribution in machine learning.
A value for n-person games
Shapley, L. S · 1953
Earlier work this paper cites.
Statistical modeling: The two cultures (with comments and a rejoinder by the author)
Breiman, L. et al · 2001
Earlier work this paper cites.
Regularization and variable selection via the elastic net
Zou, H. and Hastie, T · 2005
Earlier work this paper cites.
Explaining prediction models and individual predictions with feature contributions
Štrumbelj, E. and Kononenko, I · 2014
Earlier work this paper cites.
Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
Datta, A., Sen, S., and Zick, Y · 2016
Earlier work this paper cites.
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
Earlier work this paper cites.
Explainable machine learning prediction of synergistic drug combinations for precision cancer medicine
Janizek, J. D., Celik, S., and Lee, S.-I · 2018
Earlier work this paper cites.
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia
Lee, S.-I., Celik, S., Logsdon, B. A., Lundberg, S. M., Martins, T. J., Oehler, V. G., Estey, E. H., Miller, C. P., Chien, S., Dai, J., et al · 2018
Cited alongside, same era.
Functional genomic landscape of acute myeloid leukaemia
Tyner, J. W., Tognon, C. E., Bottomly, D., Wilmot, B., Kurtz, S. E., Savage, S. L., Long, N., Schultz, A. R., Traer, E., Abel, M., et al · 2018
Cited alongside, same era.
Aas, K., Jullum, M., and Løland, A · 2019
Cited alongside, same era.
Asymmetric shapley values: incorporating causal knowledge into model-agnostic explainability
Frye, C., Feige, I., and Rowat, C · 2019
Cited alongside, same era.
Data shapley: Equitable valuation of data for machine learning
Explaining black box decisions by shapley cohort refinement
Mase, M., Owen, A. B., and Seiler, B · 2019
Later among the works it cites.
The many shapley values for model explanation
Sundararajan, M. and Najmi, A · 2019
Later among the works it cites.
Explainable machine learning in deployment
Bhatt, U., Xiang, A., Sharma, S., Weller, A., Taly, A., Jia, Y., Ghosh, J., Puri, R., Moura, J. M., and Eckersley, P · 2020
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Understanding global feature contributions through additive importance measures
Covert, I., Lundberg, S., and Lee, S.-I · 2020
Closest in time.
Problems with shapley-value-based explanations as feature importance measures
Kumar, I. E., Venkatasubramanian, S., Scheidegger, C., and Friedler, S · 2020
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Ghorbani, A. and Zou, J · 2019
Cited alongside, same era.
Feature relevance quantification in explainable ai: A causality problem
Janzing, D., Minorics, L., and Blöbaum, P · 2019
Cited alongside, same era.
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From local explanations to global understanding with explainable ai for trees
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
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