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Over the last few years, the Shapley value, a solution concept from cooperative game theory, has found numerous applications in machine learning.
A Value for N-Person Games
Lloyd Shapley · 1953
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Weighted Voting Doesn’t Work: A Mathematical Analysis
John F Banzhaf III · 1964
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Multilinear Extensions of Games
Guillermo Owen · 1972
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Detection of influential observation in linear regression
R Dennis Cook · 1977
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Values of Games with a Priori Unions
Guilliermo Owen · 1977
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A Value for Cooperative Games with Levels Structure of Cooperation
Eyal Winter · 1989
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An Introduction to Variable and Feature Selection
Isabelle Guyon and André Elisseeff · 2003
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Configuration Values: Extensions of the Coalitional Owen Value
Josune Albizuri, Jesús Aurrecoechea, and José Manuel Zarzuelo · 2006
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Feature Selection via Coalitional Game Theory
Shay Cohen, Gideon Dror, and Eytan Ruppin · 2007
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A Linear Approximation Method for the Shapley Value
Shaheen S Fatima, Michael Wooldridge, and Nicholas R Jennings · 2008
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Polynomial Calculation of the Shapley Value Based on Sampling
Javier Castro, Daniel Gómez, and Juan Tejada · 2009
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Sampling algorithms and coresets for
Anirban Dasgupta, Petros Drineas, Boulos Harb, Ravi Kumar, and Michael W Mahoney · 2009
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Computational Aspects of Cooperative Game Theory
Georgios Chalkiadakis, Edith Elkind, and Michael Wooldridge · 2011
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Feature Evaluation and Selection with Cooperative Game Theory
Xin Sun, Yanheng Liu, Jin Li, Jianqi Zhu, et al · 2012
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Bounding the Estimation Error of Sampling-based Shapley Value Approximation
Sasan Maleki, Long Tran-Thanh, Greg Hines, Talal Rahwan, and Alex Rogers · 2013
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Simulation and the Monte Carlo Method
Reuven Y Rubinstein and Dirk P Kroese · 2016
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Improving Polynomial Estimation of the Shapley Value by Stratified Random Sampling with Optimum Allocation
Javier Castro, Daniel Gómez, Elisenda Molina, and Juan Tejada · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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A Unified Approach to Interpreting Model Predictions
Scott M Lundberg and Su-In Lee · 2017
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L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data
Jianbo Chen, Le Song, Martin Wainwright, and Michael Jordan · 2018
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Finding influential training samples for gradient boosted decision trees
Boris Sharchilev, Yury Ustinovskiy, Pavel Serdyukov, and Maarten Rijke · 2018
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Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation
Marco Ancona, Cengiz Oztireli, and Markus Gross · 2019
Cited alongside, same era.
Data Shapley: Equitable Valuation of Data for Machine Learning
Amirata Ghorbani and James Zou · 2019
Cited alongside, same era.
Estimation of the Shapley Value by Ergodic Sampling
Ferenc Illés and Péter Kerényi · 2019
Cited alongside, same era.
Towards Efficient Data Valuation Based on the Shapley Value
Ruoxi Jia, David Dao, Boxin Wang, Hubis, et al · 2019
Cited alongside, same era.
Antithetic and Monte Carlo Kernel Estimators for Partial Rankings
Maria Lomeli, Mark Rowland, Arthur Gretton, and Zoubin Ghahramani · 2019
Cited alongside, same era.
Shapley Explainability on the Data Manifold
Graphsvx: Shapley value explanations for graph neural networks
Alexandre Duval and Fragkiskos D. Malliaros · 2021
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Improving Fairness for Data Valuation in Federated Learning
Zhenan Fan, Huang Fang, Zirui Zhou, Jian Pei, et al · 2021
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Shapley Values for Feature Selection: the Good, the Bad, and the Axioms
Daniel Fryer, Inga Strümke, and Hien Nguyen · 2021
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Cga: A new feature selection model for visual human action recognition
Ritam Guha, Ali Hussain Khan, Pawan Kumar Singh, Ram Sarkar, and Debotosh Bhattacharjee · 2021
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Efficient Computation and Analysis of Distributional Shapley Values
Yongchan Kwon, Manuel A Rivas, and James Zou · 2021
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Christopher Frye, Damien de Mijolla, Tom Begley, et al · 2020
Cited alongside, same era.
Asymmetric Shapley Values: Incorporating Causal Knowledge Into Model-Agnostic Explainability
Christopher Frye, Colin Rowat, and Ilya Feige · 2020
Cited alongside, same era.
A Distributional Framework for Data Valuation
Amirata Ghorbani, Michael Kim, and James Zou · 2020
Cited alongside, same era.
Neuron Shapley: Discovering the Responsible Neurons
Amirata Ghorbani and James Zou · 2020
Cited alongside, same era.
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models
Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, and Tom Claassen · 2020
Cited alongside, same era.
Problems with Shapley-Value-Based Explanations as Feature Importance Measures
Elizabeth Kumar, Suresh Venkatasubramanian, Carlos Scheidegger, and Sorelle Friedler · 2020
Cited alongside, same era.
Shapley Values and Meta-Explanations for Probabilistic Graphical Model Inference
Yifei Liu, Chao Chen, Yazheng Liu, Xi Zhang, and Sihong Xie · 2020
Cited alongside, same era.
Yongchan Kwon and James Zou · 2021
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Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning
Jiahui Li, Kun Kuang, Baoxiang Wang, et al · 2021
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GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning
Zelei Liu, Yuanyuan Chen, Han Yu, Yang Liu, and Lizhen Cui · 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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A Multilinear Sampling Algorithm to Estimate Shapley Values
Ramin Okhrati and Aldo Lipani · 2021
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Game-Theoretic Vocabulary Selection via the Shapley Value and Banzhaf Index
Roma Patel, Marta Garnelo, Ian Gemp, Chris Dyer, and Yoram Bachrach · 2021
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The Shapley Value of Classifiers in Ensemble Games
Benedek Rozemberczki and Rik Sarkar · 2021
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Online Class-Incremental Continual Learning with Adversarial Shapley Value
Dongsub Shim, Zheda Mai, Jihwan Jeong, Scott Sanner, et al · 2021
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Flow-based Attribution in Graphical Models: A Recursive Shapley Approach
Raghav Singal, George Michailidis, and Hoiyi Ng · 2021
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A Bayesian Monte Carlo Method for Computing the Shapley Value: Application to Weighted Voting and Bin Packing Games
Sofiane Touati, Mohammed Said Radjef, and SAIS Lakhdar · 2021
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SHAQ: Incorporating Shapley Value Theory into Q-Learning for Multi-Agent Reinforcement Learning
Jianhong Wang, Jinxin Wang, Yuan Zhang, Yunjie Gu, and Tae-Kyun Kim · 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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If You Like Shapley Then You Will Love the Core
Tom Yan and Ariel D. Procaccia · 2021
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On Explainability of Graph Neural Networks via Subgraph Explorations
Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, and Shuiwang Ji · 2021
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Interpreting Multivariate Shapley Interactions in DNNs
Hao Zhang, Yichen Xie, Longjie Zheng, Die Zhang, and Quanshi Zhang · 2021
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