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Explaining complex or seemingly simple machine learning models is an important practical problem.
On the generalised distance in statistics
Mahalanobis, P. C · 1936
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A new measure of rank correlation
Kendall, M. G · 1938
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The population frequencies of species and the estimation of population parameters
Good, I. J · 1953
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A Value for N-Person Games
Shapley, L. S · 1953
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Remarks on Some Nonparametric Estimates of a Density Function
Rosenblatt, M · 1956
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Fonctions de répartition à n
Sklar, A · 1959
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Monotonic solutions of cooperative games
Young, H. P · 1985
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Extremal Principle Solutions of Games in Characteristic Function Form: Core, Chebychev and Shapley Value Generalizations
Charnes, A · 1988
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The Nadaraya-Watson kernel regression function estimator
Bierens, H. J · 1994
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An automated approach for clustering an ensemble of NMR-derived protein structures into conformationally related subfamilies
Kelley, L. A · 1996
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Clustering large data sets with mixed numeric and categorical variables
Huang, Z · 1997
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Extensions to the k-Means Algorithm for Clustering Large Data sets with Categorical variables
Huang, Z · 1998
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Smoothing parameter selection in nonparametric regression using an improved akaike information criterion
Hurvich, C. M · 1998
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A generalized mahalanobis distance for mixed data
de Leon, A. R · 2005
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Quantitative Risk Management: Concepts, Techniques and Tools
McNeil, A. J · 2006
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Strictly proper scoring rules, prediction, and estimation
Gneiting, T · 2007
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How to explain individual classification descisions
Baehrens, D · 2010
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Fast kernel conditional density estimation: A dual-tree monte carlo approach
Holmes, M. P · 2010
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Statistical methods for fighting financial crimes
Sudjianto, A · 2010
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An Efficient Explanation of Individual Classifications using Game Theory
Štrumbel, E · 2010
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Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation)
European Union · 2016
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Entity Embeddings of Categorical Variables
Guo, C · 2016
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Predicting the Future – Big Data, Machine Learning, and Clinical Medicine
Obermeyer, Z · 2016
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Why should I trust you? Explaining predictions of any classifier
Ribeiro, M. T · 2016
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Shapley effects for global sensitivity analysis: Theory and computation
Song, E · 2016
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White, D · 2012
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fastcluster: Fast hierarchical, agglomerative clustering routines for R and Python
Müllner, D · 2013
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Sobol indices and Shapley value
Owen, A. B · 2014
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Explaining prediction models and individual predictions with feature contributions
Štrumbel, E · 2014
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A mixture of generalized hyperbolic distributions
Browne, R. P · 2015
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Adaptive pointwise estimation of conditional density function
Bertin, K · 2016
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Converting high-dimensional regression to high-dimensional conditional density estimation
Izbicki, R · 2017
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Converting high-dimensional regression to high-dimensional conditional density estimation
Izbicki, R · 2017
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On Shapley value for measuring importance of dependent inputs
Owen, A. B · 2017
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L-shapley and c-shapley: Efficient model interpretation for structured data
Chen, J · 2018
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Predicting mortgage default using convolutional neural networks
Kvamme, H · 2018
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Li, X · 2018
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Interpretable Machine Learning - A Guide for Making Black Box Models explainable
Molnar, C · 2019
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Mixtures of generalized hyperbolic distributions and mixtures of skew-t distributions for model-based clustering with incomplete data
Wei, Y · 2019
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