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The Shapley value concept from cooperative game theory has become a popular technique for interpreting ML models, but efficiently estimating these values remains challenging, particularly in the model-agnostic setting.
A value for n-person games
Lloyd S Shapley · 1953
Earlier work this paper cites.
Note on a method for calculating corrected sums of squares and products
BP Welford · 1962
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Prior solutions: Extensions of convex nucleus solutions to chance-constrained games
Abraham Charnes and Daniel Granot · 1973
Earlier work this paper cites.
Coalitional and chance-constrained solutions to n-person games. i: The prior satisficing nucleolus
A Charnes and Daniel Granot · 1976
Earlier work this paper cites.
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
Earlier work this paper cites.
Approximations of pseudo-boolean functions; applications to game theory
Peter L Hammer and Ron Holzman · 1992
Earlier work this paper cites.
Economic applications of the Shapley value
Robert JJ Aumann · 1994
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
Earlier work this paper cites.
Equivalent representations of set functions
Michel Grabisch, Jean-Luc Marichal, and Marc Roubens · 2000
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Asymptotic Statistics , volume 3
Aad W Van der Vaart · 2000
Earlier work this paper cites.
Analysis of regression in game theory approach
Stan Lipovetsky and Michael Conklin · 2001
Earlier work this paper cites.
Variations on the Shapley value
Dov Monderer, Dov Samet, et al · 2002
Earlier work this paper cites.
Time-consistent Shapley value allocation of pollution cost reduction
Leon Petrosjan and Georges Zaccour · 2003
Earlier work this paper cites.
Convex Optimization
Stephen Boyd and Lieven Vandenberghe · 2004
Earlier work this paper cites.
Formulas for approximating pseudo-boolean random variables
Guoli Ding, Robert F Lax, Jianhua Chen, and Peter P Chen · 2008
Cited alongside, same era.
Polynomial calculation of the Shapley value based on sampling
Javier Castro, Daniel Gómez, and Juan Tejada · 2009
Cited alongside, same era.
Transforms of pseudo-boolean random variables
Guoli Ding, Robert F Lax, Jianhua Chen, Peter P Chen, and Brian D Marx · 2010
Cited alongside, same era.
Explaining by removing: A unified framework for model explanation
Ian Covert, Scott Lundberg, and Su-In Lee · 2011
Cited alongside, same era.
Weighted Banzhaf power and interaction indexes through weighted approximations of games
Jean-Luc Marichal and Pierre Mathonet · 2011
Cited alongside, same era.
UCI machine learning repository, 2013
Lightgbm: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
Later among the works it cites.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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A comprehensive pan-cancer molecular study of gynecologic and breast cancers
Ashton C Berger, Anil Korkut, Rupa S Kanchi, Apurva M Hegde, Walter Lenoir, Wenbin Liu, Yuexin Liu, Huihui Fan, Hui Shen, Visweswaran Ravikumar, et al · 2018
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L-Shapley and C-Shapley: Efficient model interpretation for structured data
Jianbo Chen, Le Song, Martin J Wainwright, and Michael I Jordan · 2018
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Catboost: unbiased boosting with categorical features
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin · 2018
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Moshe Lichman et al · 2013
Cited alongside, same era.
Bounding the estimation error of sampling-based Shapley value approximation
Sasan Maleki, Long Tran-Thanh, Greg Hines, Talal Rahwan, and Alex Rogers · 2013
Cited alongside, same era.
A data-driven approach to predict the success of bank telemarketing
Sérgio Moro, Paulo Cortez, and Paulo Rita · 2014
Cited alongside, same era.
Sobol’ indices and Shapley value
Art B Owen · 2014
Cited alongside, same era.
Explaining prediction models and individual predictions with feature contributions
Erik Štrumbelj and Igor Kononenko · 2014
Cited alongside, same era.
Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
Anupam Datta, Shayak Sen, and Yair Zick · 2016
Cited alongside, same era.
“Why should I trust you?” Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
Marco Ancona, Cengiz Öztireli, and Markus Gross · 2019
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Data Shapley: Equitable valuation of data for machine learning
Amirata Ghorbani and James Zou · 2019
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The explanation game: Explaining machine learning models using Shapley values
Luke Merrick and Ankur Taly · 2019
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SAM: The sensitivity of attribution methods to hyperparameters
Naman Bansal, Chirag Agarwal, and Anh Nguyen · 2020
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Damien Garreau and Ulrike von Luxburg · 2020
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Neuron Shapley: Discovering the responsible neurons
Amirata Ghorbani and James Zou · 2020
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From local explanations to global understanding with explainable AI for trees
Scott M Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee · 2020
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An analysis of LIME for text data
Dina Mardaoui and Damien Garreau · 2020
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