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The original development of Shapley values for prediction explanation relied on the assumption that the features being described were independent.
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Aas, K., Czado, C., Frigessi, A., Bakken, H.: Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics 44
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Holmes, M.P., Gray, A.G., Isbell, C.L.: Fast kernel conditional density estimation: A dual-tree Monte Carlo approach. Computational Statistics & Data Analysis 54
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Štrumbelj, E., Kononenko, I.: An Efficient Explanation of Individual Classifications using Game Theory. Journal of Machine Learning Research 11
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Panagiotelis, A., Czado, C., Joe, H.: Pair copula constructions for multivariate discrete data. Journal of the American Statistical Association 107
Wright, M.N., Ziegler, A.: ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software 77
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Otneim, H., Tjøstheim, D.: Conditional density estimation using the local Gaussian correlation. Statistics and Computing 28
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Sahin, E. and Saul, C. J. and Ozsarfati, E. and and Yilmaz, A.: Abalone Life Phase Classification with Deep Learning. In: 2018 5th International Conference on Soft Computing & Machine Intelligence (ISCMI). pp. 163–167 (2018)
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Štrumbelj, E., Kononenko, I.: Explaining prediction models and individual predictions with feature contributions. Knowledge and Information Systems 41
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Cooke, R., Kurowicka, D., Wilson, K.: Sampling, conditionalizing, counting, merging, searching regular vines. Journal of Multivariate Analysis 138
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Stöber, J., Hong, H., Czado, C. Ghosh, P.: Comorbidity of chronic diseases in the elderly: Patterns identified by a copula design for mixed responses. Computational Statistics & Data Analysis 88
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Bertin, K., Lacour, C., Rivoirard, V.: Adaptive pointwise estimation of conditional density function. Ann. Inst. H. Poincaré Probab. Statist. 52
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Haff, I.H., Aas, K., Frigessi, A., Lacal, V.: Structure learning in Bayesian Networks using regular vines. Computational Statistics & Data Analysis 101
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Nagler, T., Czado, C.: Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas. Journal of Multivariate Analysis 151
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Izbicki, R., B. Lee, A.: Converting high-dimensional regression to high-dimensional conditional density estimation. Electron. J. Statist. 11
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2019
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Chang, B., Joe, H.: Prediction based on conditional distributions of vine copulas. Computational Statistics & Data Analysis 139
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Claudia Czado: Comparing Regular Vine Copula Models. In: Analyzing Dependent Data with Vine Copulas, Lecture Notes in Statistics, vol. 222. Springer-Verlag, Cambridge (2019)
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Smith, J. S. and Wu, B. and Wilamowski, B. M.: Neural Network Training With Levenberg-Marquardt and Adaptable Weight Compression. IEEE Transactions on Neural Networks and Learning Systems 30
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Chen, H., Janizek, J.D., Lundberg, S., Lee, S.I.: True to the model or true to the data? In: 2020 ICML Workshop on Human Interpretability in Machine Learning (WHI 2020) (2020)
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Nagler, T., Vatter, T.: rvinecopulib: High Performance Algorithms for Vine Copula Modeling (2020), R package version 0.5.4.1.0
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Sellereite, N., Jullum, M.: shapr: An R-package for explaining machine learning models with dependence-aware Shapley values. Journal of Open Source Software 5
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