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Feature importance scores are ubiquitous tools for understanding the predictions of machine learning models.
Global aggregations of local explanations for black box models, 2019
Ilse van der Linden, Hinda Haned, and Evangelos Kanoulas · 1907
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Feature selection via coalitional game theory
Shay B. Cohen, Gideon Dror, and Eytan Ruppin · 1939
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Sequential tests of statistical hypotheses
Abraham Wald · 1945
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A value for n-person games, march 1952
Lloyd Shapley · 1952
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Design and analysis of randomized clinical trials requiring prolonged observation of each patient. i. introduction and design
R Peto, MC Pike, P Armitage, NE Breslow, DR Cox, SV Howard, N Mantel, K McPherson, J Peto, and PG Smith · 1976
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Group sequential methods in the design and analysis of clinical trials
Stuart J. Pocock · 1977
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A simple sequentially rejective multiple test procedure
Sture Holm · 1979
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A multiple testing procedure for clinical trials
Peter C. O’Brien and Thomas R. Fleming · 1979
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Multiparameter hypothesis testing and acceptance sampling
Roger L. Berger · 1982
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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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
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Least angle regression
Bradley Efron, Trevor Hastie, Iain Johnstone, and Robert Tibshirani · 2004
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The shapley value of phylogenetic trees
Claus-Jochen Haake, Akemi Kashiwada, and Francis Edward Su · 2007
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Determining the top-k nodes in social networks using the shapley value
Ramasuri Narayanam and Y. Narahari · 2008
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Conditional variable importance for random forests
Carolin Strobl, Anne-Laure Boulesteix, Thomas Kneib, Thomas Augustin, and Achim Zeileis · 2008
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Explaining instance classifications with interactions of subsets of feature values
Erik Strumbelj, Igor Kononenko, and Marko Robnik-Sikonja · 2009
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The solution path of the generalized lasso
Ryan J. Tibshirani and Jonathan Taylor · 2011
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Explaining prediction models and individual predictions with feature contributions
Erik Strumbelj and Igor Kononenko · 2014
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Distilling the knowledge in a neural network, 2015
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Statistical learning and selective inference
Jonathan Taylor and Robert J. Tibshirani · 2015
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Quantifying uncertainty in random forests via confidence intervals and hypothesis tests
Lucas Mentch and Giles Hooker · 2016
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”why should I trust you?”: Explaining the predictions of any classifier
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Distribution-free predictive inference for regression, 2017
Jing Lei, Max G’Sell, Alessandro Rinaldo, Ryan J. Tibshirani, and Larry Wasserman · 2017
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A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee · 2017
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An improved shapley value method for a green supply chain income distribution mechanism
Z. Xu, Z. Peng, L. Yang, and X. Chen · 2018
Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance
Giles Hooker, Lucas Mentch, and Siyu Zhou · 2021
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Identifying top-k players in cooperative games via shapley bandits
Patrick Kolpaczki, Viktor Bengs, and Eyke Hüllermeier · 2021
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Shapley counterfactual credits for multi-agent reinforcement learning
Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Fei Wu, and Jun Xiao · 2021
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The shapley value of classifiers in ensemble games
Benedek Rozemberczki and Rik Sarkar · 2021
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Application of machine learning in banking and finance: a bibliometric analysis
Rahul Dubey and Arti Chandani · 2022
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Shapley value methods for attribution modeling in online advertising, 2018
Kaifeng Zhao, Seyed Hanif Mahboobi, and Saeed R. Bagheri · 2018
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Machine Learning Risk Assessments in Criminal Justice Settings
Richard Berk · 2019
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All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously
Aaron Fisher, Cynthia Rudin, and Francesca Dominici · 2019
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Data shapley: Equitable valuation of data for machine learning, 2019
Amirata Ghorbani and James Zou · 2019
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Rank verification for exponential families
Kenneth Hung and William Fithian · 2019
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Improving kernelshap: Practical shapley value estimation via linear regression
Ian Covert and Su-In Lee · 2020
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Developing a sustainable operational management system using hybrid Shapley value and Multimoora method: case study petrochemical supply chain
Alireza Goli and Hatam Mohammadi · 2022
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Global counterfactual explanations: Investigations, implementations and improvements, 2022
Dan Ley, Saumitra Mishra, and Daniele Magazzeni · 2022
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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 · 2022
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Sampling permutations for shapley value estimation
Rory Mitchell, Joshua Cooper, Eibe Frank, and Geoffrey Holmes · 2022
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Interpretable Machine Learning
Christoph Molnar · 2022
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Stabilizing estimates of shapley values with control variates
Jeremy Goldwasser and Giles Hooker · 2023
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Crime prediction using machine learning and deep learning: A systematic review and future directions
Varun Mandalapu, Lavanya Elluri, Piyush Vyas, and Nirmalya Roy · 2023
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Feature importance: A closer look at shapley values and loco, 2023
Isabella Verdinelli and Larry Wasserman · 2023
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Effective pruning for top-k feature search on the basis of shap values
Lisa Chabrier, Anton Crombach, Sergio Peignier, and Christophe Rigotti · 2024
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Shap@k: Efficient and probably approximately correct (pac) identification of top-k features
Shashank Kariyappa, Lefteris Tsepenekas, Fabien Lécué, and Daniele Magazzeni · 2024
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Confident feature ranking
Bitya Neuhof and Yuval Benjamini · 2024
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Sharp: A novel feature importance framework for ranking, 2024
Venetia Pliatsika, Joao Fonseca, Kateryna Akhynko, Ivan Shevchenko, and Julia Stoyanovich · 2024
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Gaussian rank verification, 2025
Jeremy Goldwasser, Will Fithian, and Giles Hooker · 2025
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S-lime: Stabilized-lime for model explanation
Zhengze Zhou, Giles Hooker, and Fei Wang · 2025
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