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In this work, we focus on the use of influence functions to identify relevant training examples that one might hope "explain" the predictions of a machine learning model.
The infinitesimal jackknife
Louis A Jaeckel · 1972
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The influence curve and its role in robust estimation
Frank R Hampel · 1974
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The influence function as an aid in outlier detection in discriminant analysis
Norm A Campbell · 1978
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Residuals and influence in regression
R Dennis Cook and Sanford Weisberg · 1982
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The jackknife, the bootstrap, and other resampling plans , volume 38
Bradley Efron · 1982
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
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Principal component analysis based on robust estimators of the covariance or correlation matrix: influence functions and efficiencies
Christophe Croux and Gentiane Haesbroeck · 2000
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Prototype selection for interpretable classification
Jacob Bien and Robert Tibshirani · 2011
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New insights and perspectives on the natural gradient method
James Martens · 2014
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Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
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Optimizing neural networks with kronecker-factored approximate curvature
James Martens and Roger Grosse · 2015
Cited alongside, same era.
Examples are not enough, learn to criticize! criticism for interpretability
Been Kim, Rajiv Khanna, and Oluwasanmi O Koyejo · 2016
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The mythos of model interpretability
Zachary C Lipton · 2016
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Second-order stochastic optimization for machine learning in linear time
Naman Agarwal, Brian Bullins, and Elad Hazan · 2017
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Towards a rigorous science of interpretable machine learning
Stronger data poisoning attacks break data sanitization defenses
Pang Wei Koh, Jacob Steinhardt, and Percy Liang · 2018
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Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
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Optimal subsampling with influence functions
Daniel Ting and Eric Brochu · 2018
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The effects of example-based explanations in a machine learning interface
Carrie J. Cai, Jonas Jongejan, and Jess Holbrook · 2019
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Explaining image classifiers by adaptive dropout and generative in-filling
Chun-Hao Chang, Elliot Creager, Anna Goldenberg, and David Duvenaud · 2019
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Data shapley: Equitable valuation of data for machine learning
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Finale Doshi-Velez and Been Kim · 2017
Cited alongside, same era.
European union regulations on algorithmic decision-making and a “right to explanation”
Bryce Goodman and Seth Flaxman · 2017
Cited alongside, same era.
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres · 2017
Cited alongside, same era.
Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Amirata Ghorbani and James Y. Zou · 2019
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Interpreting black box predictions using fisher kernels
Rajiv Khanna, Been Kim, Joydeep Ghosh, and Sanmi Koyejo · 2019
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On the accuracy of influence functions for measuring group effects
Pang Wei Koh, Kai-Siang Ang, Hubert HK Teo, and Percy Liang · 2019
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Effects of influence on user trust in predictive decision making
Jianlong Zhou, Zhidong Li, Huaiwen Hu, Kun Yu, Fang Chen, Zelin Li, and Yang Wang · 2019
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