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Feature attributions based on the Shapley value are popular for explaining machine learning models; however, their estimation is complex from both a theoretical and computational standpoint.
“A value for n-person games”
Lloyd Shapley · 1953
Earlier work this paper cites.
“Multilinear extensions of games”
Guillermo Owen · 1972
Earlier work this paper cites.
“Computational complexity of the game theory approach to cost allocation for a tree”
Nimrod Megiddo · 1978
Earlier work this paper cites.
“Value theory without efficiency”
Pradeep Dubey, Abraham Neyman and Robert Weber · 1981
Earlier work this paper cites.
“Monotonic solutions of cooperative games”
H Young · 1985
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.
“The Shapley value for cooperative games under precedence constraints”
Ulrich Faigle and Walter Kern · 1992
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“Economic applications of the Shapley value”
Robert Aumann · 1994
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“On the complexity of cooperative solution concepts”
Xiaotie Deng and Christos Papadimitriou · 1994
Earlier work this paper cites.
“The family of least square values for transferable utility games”
Luis Ruiz, Federico Valenciano and Jose Zarzuelo · 1998
Earlier work this paper cites.
“Asymptotic statistics”
Aad Van · 2000
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“Random forests”
Leo Breiman · 2001
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“Analysis of regression in game theory approach”
Stan Lipovetsky and Michael Conklin · 2001
Earlier work this paper cites.
“Cost allocation for a tree network with heterogeneous customers”
Daniel Granot, Jeroen Kuipers and Sunil Chopra · 2002
Earlier work this paper cites.
“Using GPUs for machine learning algorithms”
Dave Steinkraus, Ian Buck and PY Simard · 2005
Earlier work this paper cites.
“A linear approximation method for the Shapley value”
Shaheen Fatima, Michael Wooldridge and Nicholas Jennings · 2008
Earlier work this paper cites.
“Polynomial calculation of the Shapley value based on sampling”
Javier Castro, Daniel Gómez and Juan Tejada · 2009
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“The systemic importance of financial institutions”
Nikola Tarashev, Claudio Borio and Kostas Tsatsaronis · 2009
Earlier work this paper cites.
“Explaining instance classifications with interactions of subsets of feature values”
Erik Štrumbelj, Igor Kononenko and M Šikonja · 2009
Earlier work this paper cites.
“An efficient explanation of individual classifications using game theory”
Erik Strumbelj and Igor Kononenko · 2010
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“The Shapley and Banzhaf values in microarray games”
Roberto Lucchetti, Stefano Moretti, Fioravante Patrone and Paola Radrizzani · 2010
Earlier work this paper cites.
“Statistical analysis of the Shapley value for microarray games”
Stefano Moretti · 2010
Earlier work this paper cites.
“On using very large target vocabulary for neural machine translation”
Sebastien Jean, Kyunghyun Cho, Roland Memisevic and Yoshua Bengio · 2014
Earlier work this paper cites.
“Explaining prediction models and individual predictions with feature contributions”
Erik Štrumbelj and Igor Kononenko · 2014
Earlier work this paper cites.
“Deep learning”
Yann LeCun, Yoshua Bengio and Geoffrey Hinton · 2015
Earlier work this paper cites.
“Addressing the computational issues of the Shapley value with applications in the smart grid”, 2015
Sasan Maleki · 2015
Earlier work this paper cites.
“Values of non-atomic games”
Robert Aumann and Lloyd Shapley · 2015
Earlier work this paper cites.
“XGBoost: A scalable tree boosting system”
Tianqi Chen and Carlos Guestrin · 2016
Earlier work this paper cites.
“" Why should I trust you?" Explaining the predictions of any classifier”
Marco Ribeiro, Sameer Singh and Carlos Guestrin · 2016
Earlier work this paper cites.
“Layer-wise relevance propagation for neural networks with local renormalization layers”
Alexander Binder et al · 2016
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.
“Risk attribution using the Shapley value: Methodology and policy applications”
Nikola Tarashev, Kostas Tsatsaronis and Claudio Borio · 2016
Cited alongside, same era.
“Simulation and the Monte Carlo method”
Reuven Rubinstein and Dirk Kroese · 2016
Cited alongside, same era.
“Mastering the game of Go without human knowledge”
David Silver et al · 2017
Cited alongside, same era.
“Deepstack: Expert-level artificial intelligence in heads-up no-limit poker”
Matej Moravcik et al · 2017
Tom Heskes, Evi Sijben, Ioan Bucur and Tom Claassen · 2020
Later among the works it cites.
“True to the Model or True to the Data?”
Hugh Chen, Joseph Janizek, Scott Lundberg and Su-In Lee · 2020
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“Shapley Explanation Networks”
Rui Wang, Xiaoqian Wang and David Inouye · 2020
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“Visualizing the Impact of Feature Attribution Baselines”
Pascal Sturmfels, Scott Lundberg and Su-In Lee · 2020
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“The Explanation Game: Explaining Machine Learning Models Using Shapley Values”
Luke Merrick and Ankur Taly · 2020
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Cited alongside, same era.
“Towards a rigorous science of interpretable machine learning”
Finale Doshi-Velez and Been Kim · 2017
Cited alongside, same era.
“A unified approach to interpreting model predictions”
Scott Lundberg and Su-In Lee · 2017
Cited alongside, same era.
“Learning important features through propagating activation differences”
Avanti Shrikumar, Peyton Greenside and Anshul Kundaje · 2017
Cited alongside, same era.
“Axiomatic attribution for deep networks”
Mukund Sundararajan, Ankur Taly and Qiqi Yan · 2017
Cited alongside, same era.
“Shapley Value: its algorithms and application to supply chains”
Daniela Landinez-Lamadrid et al · 2017
Cited alongside, same era.
“Interpretable explanations of black boxes by meaningful perturbation”
Ruth Fong and Andrea Vedaldi · 2017
Cited alongside, same era.
Christopher Frye et al · 2020
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“Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability”
Christopher Frye, Colin Rowat and Ilya Feige · 2020
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“A Projected Stochastic Gradient Algorithm for Estimating Shapley Value Applied in Attribute Importance”
Grah Simon and Thouvenot Vincent · 2020
Later among the works it cites.
“Efficient nonparametric statistical inference on population feature importance using Shapley values”
Brian Williamson and Jean Feng · 2020
Later among the works it cites.
“Understanding global feature contributions with additive importance measures”
Ian Covert, Scott Lundberg and Su-In Lee · 2020
Later among the works it cites.
“Developing an interpretable schizophrenia deep learning classifier on fMRI and sMRI using a patient-centered DeepSHAP”
Jacob Reiter · 2020
Later among the works it cites.
“Concept bottleneck models”
Pang Koh et al · 2020
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“Neuron Shapley: Discovering the responsible neurons”
Amirata Ghorbani and James Zou · 2020
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“Ls-tree: Model interpretation when the data are linguistic”
Jianbo Chen and Michael Jordan · 2020
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“Highly accurate protein structure prediction with AlphaFold”
John Jumper et al · 2021
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“Explaining by removing: A unified framework for model explanation”
Ian Covert, Scott Lundberg and Su-In Lee · 2021
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“Explaining a series of models by propagating local feature attributions”
Hugh Chen, Scott Lundberg and S Lee · 2021
Later among the works it cites.
“A multilinear sampling algorithm to estimate Shapley values”
Ramin Okhrati and Aldo Lipani · 2021
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“FastSHAP: Real-Time Shapley Value Estimation”
Neil Jethani et al · 2021
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“Learning Baseline Values for Shapley Values”
Jie Ren, Zhanpeng Zhou, Qirui Chen and Quanshi Zhang · 2021
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“Shapley flow: A graph-based approach to interpreting model predictions”
Jiaxuan Wang, Jenna Wiens and Scott Lundberg · 2021
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“Improving KernelSHAP: Practical Shapley value estimation using linear regression”
Ian Covert and Su-In Lee · 2021
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“Sampling Permutations for Shapley Value Estimation”
Rory Mitchell, Joshua Cooper, Eibe Frank and Geoffrey Holmes · 2021
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“Attention flows are Shapley value explanations”
Kawin Ethayarajh and Dan Jurafsky · 2021
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“Improved Feature Importance Computations for Tree Models: Shapley vs. Banzhaf”
Adam Karczmarz, Anish Mukherjee, Piotr Sankowski and Piotr Wygocki · 2021
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Alexey Miroshnikov, Konstandinos Kotsiopoulos and Arjun Kannan · 2021
Later among the works it cites.
“Learning to Estimate Shapley Values with Vision Transformers”
Ian Covert, Chanwoo Kim and Su-In Lee · 2022
Closest in time.
“The Shapley Value in Machine Learning”
Benedek Rozemberczki et al · 2022
Closest in time.
“Variations on the Shapley value”
Dov Monderer and Dov Samet · 2076
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