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Recent work in model-agnostic explanations of black-box machine learning has demonstrated that interpretability of complex models does not have to come at the cost of accuracy or model flexibility.
A deductive approach to program synthesis
Zohar Manna and Richard Waldinger · 1980
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Optimization by simulated annealing
S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi · 1983
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Extracting tree-structured representations of trained networks
Mark W Craven and Jude W Shavlik · 1996
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Functional genetic programming with combinators
Forrest Briggs and Melissa O’neill · 2006
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Church: a language for generative models
Noah D. Goodman, Vikash K. Mansinghka, Daniel Roy, Keith Bonawitz, and Joshua B. Tenenbaum · 2008
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Natively probabilistic computation
Vikash Kumar Mansinghka · 2009
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How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller · 2010
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An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models
Johan Huysmans, Karel Dejaeger, Christophe Mues, Jan Vanthienen, and Bart Baesens · 2010
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Learning programs: A hierarchical bayesian approach
Percy Liang, Michael I Jordan, and Dan Klein · 2010
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UCI machine learning repository, 2013
M. Lichman · 2013
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The bayesian case model: A generative approach for case-based reasoning and prototype classification
Been Kim, Cynthia Rudin, and Julie A Shah · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Cited alongside, same era.
Neural programmer: Inducing latent programs with gradient descent
Arvind Neelakantan, Quoc V Le, and Ilya Sutskever · 2015
Later among the works it cites.
Supersparse linear integer models for optimized medical scoring systems
Berk Ustun and Cynthia Rudin · 2015
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Fulton Wang and Cynthia Rudin · 2015
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Terpret: A probabilistic programming language for program induction
Alexander L Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, and Daniel Tarlow · 2016
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Interpretable decision sets: A joint framework for description and prediction
Himabindu Lakkaraju, Stephen H. Bach, and Jure Leskovec · 2016
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Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, and David Madigan · 2015
Cited alongside, same era.
"why should i trust you?": Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin
Cited in the paper.
Model-agnostic interpretability of machine learning
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin
Cited in the paper.
Sebastian Riedel, Matko Bošnjak, and Tim Rocktäschel · 2016
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