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We discuss promising recent contributions on quantifying feature relevance using Shapley values, where we observed some confusion on which probability distribution is the right one for dropped features.
On the generalised distance in statistics
P. C. Mahalanobis · 1936
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A value for n-person games
L. Shapley · 1953
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Extremal Principle Solutions of Games in Characteristic Function Form: Core, Chebychev and Shapley Value Generalizations
A. Charnes, B. Golany, M. Keane, and J. Rousseau · 1988
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Causality
J. Pearl · 2000
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Greedy function approximation: A gradient boosting machine
J. H. Friedman · 2001
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Paths and consistency in additive cost sharing
E. J. Friedman · 2004
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Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
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A Public Domain Dataset for Human Activity Recognition Using Smartphones
D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz · 2013
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers
A. Binder, G. Montavon, S. Lapuschkin, K. R. Müller, and W. Samek · 2016
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Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
A. Datta, S. Sen, and Y. Zick · 2016
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”why should i trust you?”: Explaining the predictions of any classifier
M. Ribeiro and C. Singh, S.and Guestrin · 2016
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Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition
M. Sharif, S. Bhagavatula, L. Bauer, and M. K. Reiter · 2016
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Not Just A Black Box: Learning Important Features Through Propagating Activation Differences
A. Shrikumar, P. Greenside, and A. Kundaje · 2016
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T. B. Brown, D. Mané, A. Roy, M. Abadi, and J. Gilmer · 2018
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Robust Physical-World Attacks on Deep Learning Visual Classification
K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, C. Xiao, A. Prakash, T. Kohno, and D. Song · 2018
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Adversarial examples in the physical world
A. Kurakin, I. J. Goodfellow, and Samy Bengio · 2018
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Consistent individualized feature attribution for tree ensembles
S. Lundberg, G. Erion, and S. Lee · 2018
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K. Aas, M. Jullum, and A. Løland · 2019
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Avoiding discrimination through causal reasoning
N. Kilbertus, M. Rojas-Carulla, G. Parascandolo, M. Hardt, D. Janzing, and B. Schölkopf · 2017
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A unified approach to interpreting model predictions
S. Lundberg and S. Lee · 2017
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Axiomatic Attribution for Deep Networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
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Fairness and Machine Learning
S. Barocas, M. Hardt, and A. Narayanan · 2018
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Neural network attributions: A causal perspective
A. Chattopadhyay, P. Manupriya, A. Sarkar, and V. Balasubramanian · 2019
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Interpretable Machine Learning
C. Molnar · 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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