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In recent years, many Machine Learning (ML) explanation techniques have been designed using ideas from cooperative game theory.
A value for n-person games,
L. S. Shapley, · 1953
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Weighted voting doesn’t work: a mathematical analysis
J.F. Banzhaf, · 1965
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Cooperative games with coalition structure
R.J. Aumann and J. Dréze, · 1974
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Values of games with a priori unions
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A. Shiryaev,
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Modification of the Banzhaf-Coleman index for games with apriory unions
G. Owen, · 1982
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The modified Banzhaf value for games with coalition structure: an axiomatic characterization
R. Amer, F. Carreras and J.M. Giménez, · 1995
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Real Analysis: Modern Techniques and Their Applications ,
G. B. Folland, · 1999
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L. Breiman, · 2001
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Greedy function approximation: a gradient boosting machine,
J. H. Friedman, · 2001
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Modification of the Banzhaf value for games with a coalition structure
J.M. Alonso–Meijide and M.G. Fiestras–Janeiro, · 2002
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T. M. Cover and J. A. Thomas, · 2006
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M. H. Kalos and P. A. Whitlock,
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Polynomial calculation of the Shapley value based on sampling
J. Castro, D. Gómez and J. Tejada, · 2009
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The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients
I. C. Yeh and C. H. Lien, · 2009
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Detecting novel associations in large data sets
D. N. Reshef, Y.A. Reshef, H. K. Finucane, R. S. Grossman, G. McVean, P. J. Turnbaugh, E. S. Lander, M. Mitzenmacher and P. C. Sabeti, · 2011
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Accurate intelligible models with pairwise interactions,
Y. Lou, R. Caruana, J. Gehrke and G. Hooker, · 2013
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A general method for visualizing and explaining black-box regression models
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Explaining prediction models and individual predictions with feature contributions
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Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation
A. Goldstein, A. Kapelner, J. Bleich and E. Pitkin, · 2015
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T. Hastie, R. Tibshirani and J. Friedman,
2016
From local explanations to global understanding with explainable AI for trees,
S. M. Lundberg, G. Erion, H. Chen, A. DeGrave, J. M. Prutkin, B. Nair, R. Katz, J. Himmelfarb, N. Bansal, and S.-I. Lee, · 2019
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Consistent individualized feature attribution for tree ensembles,
S. M. Lundberg, G. G. Erion and S.-I. Lee, · 2019
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The Many Shapley Values for Model Explanation
M. Sundararajan and A. Najmi, · 2019
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Causal Interpretations of Black-Box Models,
Q. Zhao and T. Hastie, · 2019
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K. Aas, M. Jullum and A. Løland, · 2020
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Measuring dependence powerfully and equitably
Y. A. Reshef, D.N. Reshef, H. K. Finucane, P. C. Sabeti and M. Mitzenmacher, · 2016
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“Why should I trust you?” Explaining the predictions of any classifier,
M. T. Ribeiro, S. Singh and C. Guestrin, · 2016
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New characterizations of the Owen and Banzhaf–Owen values using the intracoalitional balanced contributions property,
S. Lorenzo-Freire, · 2017
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A unified approach to interpreting model predictions,
S. M. Lundberg and S.-I. Lee, · 2017
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An Introduction to Machine Learning Interpretability , O’Reilly. (2018)
P. Hall and N. Gill, · 2018
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Locally interpretable models and effects based on supervised partitioning (LIME-SUP),
L. Hu, J. Chen, V.N. Nair and A. Sudjianto, · 2018
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True to the Model or True to the Data
H. Chen, J. Danizek, S. M. Lundberg and S.-I. Lee, · 2020
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D. C. Elton, Self-explaining AI as an alternative to interpretable AI,
2020
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Shapley Flow: A Graph-based Approach to Interpreting Model Predictions
J. Wang, J. Wiens and S. M. Lundberg, · 2020
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Explaining predictive models using Shapley values and non-parametric vine copulas,
K. Aas, T. Nagler, M. Jullum and A. Løland, · 2021
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Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application
H. Ji, K. Lafata, Y. Mowery, D. Brizel, A. L. Bertozzi, F.-F. Yin and C. Wang, · 2021
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Efficient and simple prediction explanations with groupShapley: a practical perspective,
M. Jullum, A. Redelmeier and K. Aas, · 2021
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A. Miroshnikov, K. Kotsiopoulos, K. Filom and A. Ravi Kannan, · 2021
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Algorithms to estimate Shapley value feature attributions
H. Chen, I. C. Covert, S. M. Lundberg and S.-I. Lee, · 2022
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Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features,
L. H. B. Olsen, I. K. Glad, M. Jullum and K. Aas, · 2022
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On marginal feature attributions of tree-based models,
K. Filom, A. Miroshnikov, K. Kotsiopoulos and A. Ravi Kannan, · 2023
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