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Researchers have proposed a wide variety of model explanation approaches, but it remains unclear how most methods are related or when one method is preferable to another.
A value for n-person games
Lloyd S Shapley · 1953
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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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An introduction to variable and feature selection
Isabelle Guyon and André Elisseeff · 2003
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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 Štrumbelj, Igor Kononenko, and M Robnik Šikonja · 2009
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An efficient explanation of individual classifications using game theory
Erik Štrumbelj and Igor Kononenko · 2010
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Sobol’ indices and Shapley value
Art B Owen · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Object detectors emerge in deep scene cnns
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
Anupam Datta, Shayak Sen, and Yair Zick · 2016
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The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2016
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"Why should I trust you?" Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Not just a black box: Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, and Anshul Kundaje · 2016
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Shapley effects for global sensitivity analysis: Theory and computation
Eunhye Song, Barry L Nelson, and Jeremy Staum · 2016
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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2017
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Real time image saliency for black box classifiers
Piotr Dabkowski and Yarin Gal · 2017
Cited alongside, same era.
Adversarial localization network
Lijie Fan, Shengjia Zhao, and Stefano Ermon · 2017
Cited alongside, same era.
Interpretable explanations of black boxes by meaningful perturbation
Ruth C Fong and Andrea Vedaldi · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Tim Miller, Piers Howe, and Liz Sonenberg · 2017
Cited alongside, same era.
Understanding deep networks via extremal perturbations and smooth masks
Ruth Fong, Mandela Patrick, and Andrea Vedaldi · 2019
Later among the works it cites.
Asymmetric shapley values: incorporating causal knowledge into model-agnostic explainability
Christopher Frye, Ilya Feige, and Colin Rowat · 2019
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Please stop permuting features: An explanation and alternatives
Giles Hooker and Lucas Mentch · 2019
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Feature relevance quantification in explainable AI: A causality problem
Dominik Janzing, Lenon Minorics, and Patrick Blöbaum · 2019
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The explanation game: Explaining machine learning models with cooperative game theory
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Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Cited alongside, same era.
Visualizing deep neural network decisions: Prediction difference analysis
Luisa M Zintgraf, Taco S Cohen, Tameem Adel, and Max Welling · 2017
Cited alongside, same era.
Peeking inside the black-box: A survey on explainable artificial intelligence (xai)
Amina Adadi and Mohammed Berrada · 2018
Cited alongside, same era.
Explaining image classifiers by counterfactual generation
Chun-Hao Chang, Elliot Creager, Anna Goldenberg, and David Duvenaud · 2018
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
Cited alongside, same era.
Learning to explain: An information-theoretic perspective on model interpretation
Jianbo Chen, Le Song, Martin J Wainwright, and Michael I Jordan · 2018
Cited alongside, same era.
Luke Merrick and Ankur Taly · 2019
Later among the works it cites.
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2019
Later among the works it cites.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
Later among the works it cites.
Cxplain: Causal explanations for model interpretation under uncertainty
Patrick Schwab and Walter Karlen · 2019
Later among the works it cites.
The many shapley values for model explanation
Mukund Sundararajan and Amir Najmi · 2019
Later among the works it cites.
Infomask: Masked variational latent representation to localize chest disease
Saeid Asgari Taghanaki, Mohammad Havaei, Tess Berthier, Francis Dutil, Lisa Di Jorio, Ghassan Hamarneh, and Yoshua Bengio · 2019
Later among the works it cites.
Understanding global feature contributions with additive importance measures
Ian Covert, Scott Lundberg, and Su-In Lee · 2020
Closest in time.
Shapley-based explainability on the data manifold
Christopher Frye, Damien de Mijolla, Laurence Cowton, Megan Stanley, and Ilya Feige · 2020
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From local explanations to global understanding with explainable ai for trees
Scott M. Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M. Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee · 2020
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Restricting the flow: Information bottlenecks for attribution
Karl Schulz, Leon Sixt, Federico Tombari, and Tim Landgraf · 2020
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Efficient nonparametric statistical inference on population feature importance using Shapley values
Brian Williamson and Jean Feng · 2020
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Attribution in scale and space
Shawn Xu, Subhashini Venugopalan, and Mukund Sundararajan · 2020
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Explaining predictive models using Shapley values and non-parametric vine copulas
Kjersti Aas, Thomas Nagler, Martin Jullum, and Anders Løland · 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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Explaining by removing: A unified framework for model explanation
Ian Covert, Scott M Lundberg, and Su-In Lee · 2021
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Have we learned to explain?: How interpretability methods can learn to encode predictions in their interpretations
Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, and Rajesh Ranganath · 2021
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