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Many methods to explain black-box models, whether local or global, are additive.
Generalized Additive Models
Trevor Hastie and Rob Tibshirani. 1986 · 1986
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On sensitivity estimation for nonlinear mathematical models
Il’ya Meerovich Sobol’. 1990 · 1990
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Rule generation from neural networks
LiMin Fu. 1994 · 1994
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Extracting Tree-structured Representations of Trained Networks. In NeurIPS
Mark W. Craven and Jude W. Shavlik. 1995 · 1995
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Random forests
Leo Breiman. 2001 · 2001
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Greedy Function Approximation: A Gradient Boosting Machine
Jerome H. Friedman. 2001 · 2001
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Discovering additive structure in black box functions. In KDD
Giles Hooker. 2004 · 2004
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Model compression. In KDD
Cristian Bucilua, Rich Caruana, and Alexandru Niculescu-Mizil. 2006 · 2006
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Generalized Additive Models: An Introduction with R
Simon N. Wood. 2006 · 2006
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Predictive learning via rule ensembles
Jerome H. Friedman and Bogdan E. Popescu. 2008 · 2008
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Trevor Hastie, Robert Tibshirani, and Jerome Friedman. 2009 · 2009
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Understanding the difficulty of training deep feedforward neural networks. In AISTATS
Xavier Glorot and Yoshua Bengio. 2010 · 2010
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Prototype selection for interpretable classification
Jacob Bien and Robert Tibshirani. 2011 · 2011
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Lending Club Loan Dataset 2007-2011
Lending Club. 2011 · 2011
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Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models
Simon N. Wood. 2011 · 2011
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VIKAMINE - Open-Source Subgroup Discovery, Pattern Mining, and Analytics. In ECML PKDD
Martin Atzmueller and Florian Lemmerich. 2012 · 2012
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Intelligible models for classification and regression. In KDD
Yin Lou, Rich Caruana, and Johannes Gehrke. 2012 · 2012
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Accurate intelligible models with pairwise interactions. In KDD
Yin Lou, Rich Caruana, Johannes Gehrke, and Giles Hooker. 2013 · 2013
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On concurvity in nonlinear and nonparametric regression models
Sonia Amodio, Massimo Aria, and Antonio D’Ambrosio. 2014 · 2014
Earlier work this paper cites.
Do Deep Nets Really Need to be Deep?. In NeurIPS
Jimmy Ba and Rich Caruana. 2014 · 2014
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Distilling the Knowledge in a Neural Network. In NeurIPS Deep Learning and Representation Learning Workshop
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2014 · 2014
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Sobol’ indices and Shapley value
Art B Owen. 2014 · 2014
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps. In ICLR Workshop
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014 · 2014
Earlier work this paper cites.
Explaining prediction models and individual predictions with feature contributions
Erik Štrumbelj and Igor Kononenko. 2014 · 2014
Earlier work this paper cites.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Muller, and Wojciech Samek. 2015 · 2015
Cited alongside, same era.
Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In KDD
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad. 2015 · 2015
Cited alongside, same era.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In ICML
Sergey Ioffe and Christian Szegedy. 2015 · 2015
Cited alongside, same era.
Adam: A Method for Stochastic Optimization. In ICLR
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Cited alongside, same era.
Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model
Benjamin Letham, Cynthia Rudin, Tyler H McCormick, David Madigan, et al · 2015
Cited alongside, same era.
All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously
Aaron J. Fisher, C. Rudin, and F. Dominici. 2019 · 2019
Closest in time.
Global Explanations of Neural Networks: Mapping the Landscape of Predictions. In AIES
Mark Ibrahim, Melissa Louie, Ceena Modarres, and John Paisley. 2019 · 2019
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Human evaluation of models built for interpretability. In HCOMP
Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Samuel J Gershman, and Finale Doshi-Velez. 2019 · 2019
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Faithful and Customizable Explanations of Black Box Models. In AIES
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, and Jure Leskovec. 2019 · 2019
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InterpretML: A Unified Framework for Machine Learning Interpretability
Harsha Nori, Samuel Jenkins, Paul Koch, and Rich Caruana. 2019 · 2019
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Towards extracting faithful and descriptive representations of latent variable models
Ivan Sanchez, Tim Rocktaschel, Sebastian Riedel, and Sameer Singh. 2015 · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition. In ICLR
Karen Simonyan and Andrew Zisserman. 2015 · 2015
Cited alongside, same era.
Examples are not enough, learn to criticize! criticism for interpretability. In NeurIPS
Been Kim, Rajiv Khanna, and Oluwasanmi Koyejo. 2016 · 2016
Cited alongside, same era.
“Why Should I Trust You?": Explaining the Predictions of Any Classifier. In KDD
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Cited alongside, same era.
Learning Certifiably Optimal Rule Lists. In KDD
Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, and Cynthia Rudin. 2017 · 2017
Cited alongside, same era.
Interpreting blackbox models via model extraction. In FAT/ML Workshop
Osbert Bastani, Carolyn Kim, and Hamsa Bastani. 2017 · 2017
Cited alongside, same era.
Network Dissection: Quantifying Interpretability of Deep Visual Representations. In CVPR
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba. 2017 · 2017
Cited alongside, same era.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin. 2019 · 2019
Closest in time.
Global aggregations of local explanations for black box models
Ilse van der Linden, Hinda Haned, and Evangelos Kanoulas. 2019 · 2019
Closest in time.
Visualizing the effects of predictor variables in black box supervised learning models
Daniel W Apley and Jingyu Zhu. 2020 · 2020
Closest in time.
Evaluating and Aggregating Feature-based Model Explanations. In IJCAI
Umang Bhatt, Adrian Weller, and José M. F. Moura. 2020 · 2020
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Understanding global feature contributions through additive importance measures. In NeurIPS
Ian Covert, Scott Lundberg, and Su-In Lee. 2020 · 2020
Closest in time.
Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning. In CHI
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, and Jennifer Wortman Vaughan. 2020 · 2020
Closest in time.
Purifying interaction effects with the functional anova: An efficient algorithm for recovering identifiable additive models. In AISTATS
Benjamin Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, and Rich Caruana. 2020 · 2020
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Compositional Explanations of Neurons. In NeurIPS
Jesse Mu and Jacob Andreas. 2020 · 2020
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Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses. In NeurIPS
Kaivalya Rawal and Himabindu Lakkaraju. 2020 · 2020
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Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods. In AIES
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju. 2020 · 2020
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Tree space prototypes: Another look at making tree ensembles interpretable. In FODS
Sarah Tan, Matvey Soloviev, Giles Hooker, and Martin T. Wells. 2020 · 2020
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Efficient nonparametric statistical inference on population feature importance using Shapley values. In ICML
Brian Williamson and Jean Feng. 2020 · 2020
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How Interpretable and Trustworthy are GAMs. In KDD
Chun-Hao Chang, Sarah Tan, Ben Lengerich, Anna Goldenberg, and Rich Caruana. 2021 · 2021
Closest in time.
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations. In FAccT
Sérgio Jesus, Catarina Belém, Vladimir Balayan, João Bento, Pedro Saleiro, Pedro Bizarro, and João Gama. 2021 · 2021
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A comparison of methods for interpreting random forest models of genetic association in the presence of non-additive interactions
Alena Orlenko and Jason H Moore. 2021 · 2021
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Manipulating and measuring model interpretability. In CHI
Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer Wortman Wortman Vaughan, and Hanna Wallach. 2021 · 2021
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GLocalX-From Local to Global Explanations of Black Box AI Models
Mattia Setzu, Riccardo Guidotti, Anna Monreale, Franco Turini, Dino Pedreschi, and Fosca Giannotti. 2021 · 2021
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If you like shapley then you’ll love the core. In AAAI
Tom Yan and Ariel D Procaccia. 2021 · 2021
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Causal interpretations of black-box models
Qingyuan Zhao and Trevor Hastie. 2021 · 2021
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