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The Shapley value has been proposed as a solution to many applications in machine learning, including for equitable valuation of data.
A Value for N-Person Games
L. Shapley · 1953
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Detection of influential observation in linear regression
R. D. Cook · 1977
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On the complexity of cooperative solution concepts
X. Deng and C. H. Papadimitriou · 1994
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Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid
R. Kohavi et al · 1996
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Stability and generalization
O. Bousquet and A. Elisseeff · 2002
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An Introduction to Variable and Feature Selection
I. Guyon and A. Elisseeff · 2003
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Feature Selection Based on the Shapley Value
S. Cohen, E. Ruppin, and G. Dror · 2005
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Differential privacy
C. Dwork · 2006
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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Feature selection via coalitional game theory
S. Cohen, G. Dror, and E. Ruppin · 2007
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Polynomial calculation of the shapley value based on sampling
J. Castro, D. Gómez, and J. Tejada · 2009
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Sampling algorithms and coresets for \ \backslash ell_p regression
A. Dasgupta, P. Drineas, , et al · 2009
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Uci machine learning repository [http://archive. ics. uci. edu/ml]. irvine, ca: University of california
A. Frank and A. Asuncion · 2010
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Scikit-learn: Machine learning in python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al · 2011
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Feature Evaluation and Selection with Cooperative Game Theory
X. Sun, Y. Liu, J. Li, et al · 2012
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Stability of multi-task kernel regression algorithms
J. Audiffren and H. Kadri · 2013
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Practical differential privacy via grouping and smoothing
G. Kellaris and S. Papadopoulos · 2013
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Bounding the Estimation Error of Sampling-based Shapley Value Approximation
S. Maleki, L. Tran-Thanh, G. Hines, T. Rahwan, and A. Rogers · 2013
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Bounds on the sample complexity for private learning and private data release
A. Beimel, H. Brenner, S. P. Kasiviswanathan, and K. Nissim · 2014
Cited alongside, same era.
Understanding machine learning: From theory to algorithms
S. Shalev-Shwartz and S. Ben-David · 2014
Cited alongside, same era.
Influence in Classification via Cooperative Game Theory
A. Datta, A. Datta, et al · 2015
Cited alongside, same era.
Train faster, generalize better: Stability of stochastic gradient descent
M. Hardt, B. Recht, and Y. Singer · 2016
Cited alongside, same era.
Fdvt: Data valuation tool for facebook users
J. González Cabañas, Á. Cuevas, and R. Cuevas · 2017
Cited alongside, same era.
Understanding black-box predictions via influence functions
P. W. Koh and P. Liang · 2017
Cited alongside, same era.
Neuron Shapley: Discovering the Responsible Neurons
A. Ghorbani and J. Zou · 2020
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Problems with shapley-value-based explanations as feature importance measures
I. E. Kumar, S. Venkatasubramanian, C. Scheidegger, and S. Friedler · 2020
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Federated learning: Challenges, methods, and future directions
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith · 2020
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Estimating training data influence by tracing gradient descent
G. Pruthi, F. Liu, S. Kale, and M. Sundararajan · 2020
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Interpretable Feature Subset Selection: A Shapley Value Based Approach
S. Tripathi, N. Hemachandra, and P. Trivedi · 2020
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Efficient and fair data valuation for horizontal federated learning
S. Wei, Y. Tong, Z. Zhou, and T. Song · 2020
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A unified approach to interpreting model predictions
S. M. Lundberg and S.-I. Lee · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
Cited alongside, same era.
Revisiting unreasonable effectiveness of data in deep learning era
C. Sun, A. Shrivastava, S. Singh, and A. Gupta · 2017
Cited alongside, same era.
Privacy amplification by subsampling: tight analyses via couplings and divergences
B. Balle, G. Barthe, and M. Gaboardi · 2018
Cited alongside, same era.
Generalization bounds for uniformly stable algorithms
V. Feldman and J. Vondrak · 2018
Cited alongside, same era.
Finding influential training samples for gradient boosted decision trees
B. Sharchilev, Y. Ustinovskiy, et al · 2018
Cited alongside, same era.
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Efficient Nonparametric Statistical Inference on Population Feature Importance Using Shapley Values
B. Williamson and J. Feng · 2020
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Data valuation using reinforcement learning
J. Yoon, S. Arik, and T. Pfister · 2020
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Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression
I. Covert and S.-I. Lee · 2021
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Shapley Values for Feature Selection: the Good, the Bad, and the Axioms
D. Fryer, I. Strümke, and H. Nguyen · 2021
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Robin hood and matthew effects–differential privacy has disparate impact on synthetic data
G. Ganev, B. Oprisanu, and E. De Cristofaro · 2021
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Cga: A new feature selection model for visual human action recognition
R. Guha, A. H. Khan, et al · 2021
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Efficient computation and analysis of distributional shapley values
Y. Kwon, M. A. Rivas, and J. Zou · 2021
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A survey on federated learning systems: vision, hype and reality for data privacy and protection
Q. Li, Z. Wen, Z. Wu, S. Hu, N. Wang, et al · 2021
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GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning
Z. Liu, Y. Chen, H. Yu, Y. Liu, and L. Cui · 2021
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Game-Theoretic Vocabulary Selection via the Shapley Value and Banzhaf Index
R. Patel, M. Garnelo, I. Gemp, et al · 2021
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The Shapley Value of Classifiers in Ensemble Games
B. Rozemberczki and R. Sarkar · 2021
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The shapley value in machine learning
B. Rozemberczki, L. Watson, P. Bayer, H.-T. Yang, O. Kiss, S. Nilsson, and R. Sarkar · 2022
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