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Shapley value is a classic notion from game theory, historically used to quantify the contributions of individuals within groups, and more recently applied to assign values to data points when training machine learning models.
A value for n-person games
Lloyd S Shapley · 1953
Earlier work this paper cites.
Values of non-atomic games
Robert J Aumann and Lloyd S Shapley · 1974
Earlier work this paper cites.
The Shapley value: essays in honor of Lloyd S. Shapley
Lloyd S Shapley, Alvin E Roth, et al · 1988
Earlier work this paper cites.
On the value of private information
Jon Kleinberg, Christos H Papadimitriou, and Prabhakar Raghavan · 2001
Earlier work this paper cites.
Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Feature selection via coalitional game theory
Shay Cohen, Gideon Dror, and Eytan Ruppin · 2007
Earlier work this paper cites.
An efficient explanation of individual classifications using game theory
Igor Kononenko et al · 2010
Earlier work this paper cites.
Algorithmic stability
Shivani Agarwal · 2011
Earlier work this paper cites.
Geometric approximation algorithms
Sariel Har-Peled · 2011
Cited alongside, same era.
Impact of hba1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records
Beata Strack, Jonathan P DeShazo, Chris Gennings, Juan L Olmo, Sebastian Ventura, Krzysztof J Cios, and John N Clore · 2014
Cited alongside, same era.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Cited alongside, same era.
Uk biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age
Cathie Sudlow, John Gallacher, Naomi Allen, Valerie Beral, Paul Burton, John Danesh, Paul Downey, Paul Elliott, Jane Green, Martin Landray, et al · 2015
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.
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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A marketplace for data: An algorithmic solution
Anish Agarwal, Munther Dahleh, and Tuhin Sarkar · 2019
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Making ai forget you: Data deletion in machine learning
Antonio Ginart, Melody Guan, Gregory Valiant, and James Y Zou · 2019
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Data shapley: Equitable valuation of data for machine learning
Amirata Ghorbani and James Zou · 2019
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Efficient task-specific data valuation for nearest neighbor algorithms
Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nezihe Merve Gurel, Bo Li, Ce Zhang, Costas Spanos, and Dawn Song · 2019
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Cited alongside, same era.
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
Cited alongside, same era.
Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Zou · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gürel, Bo Li, Ce Zhang, Dawn Song, and Costas J Spanos · 2019
Later among the works it cites.
Data co-ops
Katrina Ligett, Kobbi Nissim, and Ayelet Gordon-Tapiero · 2019
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Who’s responsible? jointly quantifying the contribution of the learning algorithm and training data
Gal Yona, Amirata Ghorbani, and James Zou · 2019
Later among the works it cites.