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Understanding the inner workings of complex machine learning models is a long-standing problem and most recent research has focused on local interpretability.
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Andrea Saltelli, Stefano Tarantola, Francesca Campolongo, and Marco Ratto · 2004
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Understanding the value of features for coreference resolution
Eric Bengtson and Dan Roth · 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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Powerful bivariate genome-wide association analyses suggest the sox6 gene influencing both obesity and osteoporosis phenotypes in males
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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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The nature of statistical learning theory
Vladimir Vapnik · 2013
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Event labeling combining ensemble detectors and background knowledge
Hadi Fanaee-T and Joao Gama · 2014
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A data-driven approach to predict the success of bank telemarketing
Sérgio Moro, Paulo Cortez, and Paulo Rita · 2014
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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On Shapley value for measuring importance of dependent inputs
Art B Owen and Clémentine Prieur · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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A comprehensive pan-cancer molecular study of gynecologic and breast cancers
Ashton C Berger, Anil Korkut, Rupa S Kanchi, Apurva M Hegde, Walter Lenoir, Wenbin Liu, Yuexin Liu, Huihui Fan, Hui Shen, Visweswaran Ravikumar, et al · 2018
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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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Sobol’ indices and Shapley value
Art B Owen · 2014
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Explaining prediction models and individual predictions with feature contributions
Erik Štrumbelj and Igor Kononenko · 2014
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Bcl11a is a triple-negative breast cancer gene with critical functions in stem and progenitor cells
Walid T Khaled, Song Choon Lee, John Stingl, Xiongfeng Chen, H Raza Ali, Oscar M Rueda, Fazal Hadi, Juexuan Wang, Yong Yu, Suet-Feung Chin, et al · 2015
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The cancer genome atlas (TCGA): an immeasurable source of knowledge
Katarzyna Tomczak, Patrycja Czerwińska, and Maciej Wiznerowicz · 2015
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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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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Harvey R Fernandez, Shreyas M Gadre, Mingjun Tan, Garrett T Graham, Rami Mosaoa, Martin S Ongkeko, Kyu Ah Kim, Rebecca B Riggins, Erika Parasido, Iacopo Petrini, et al · 2018
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Sensitivity based neural networks explanations
Enguerrand Horel, Virgile Mison, Tao Xiong, Kay Giesecke, and Lidia Mangu · 2018
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Distribution-free predictive inference for regression
Jing Lei, Max G’Sell, Alessandro Rinaldo, Ryan J Tibshirani, and Larry Wasserman · 2018
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Catboost: unbiased boosting with categorical features
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin · 2018
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Kjersti Aas, Martin Jullum, and Anders Løland · 2019
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Shapley effects for sensitivity analysis with dependent inputs: bootstrap and kriging-based algorithms
Nazih Benoumechiara and Kevin Elie-Dit-Cosaque · 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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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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