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At the core of interpretable machine learning is the question of whether humans are able to make accurate predictions about a model's behavior.
Probability inequalities for sums of bounded random variables
W Hoeffding · 1963
Earlier work this paper cites.
Lazy decision trees
Jerome H. Friedman · 1996
Earlier work this paper cites.
Mining high-speed data streams
Pedro Domingos and Geoff Hulten · 2000
Earlier work this paper cites.
How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller · 2010
Earlier work this paper cites.
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
Cited alongside, same era.
The bayesian case model: A generative approach for case-based reasoning and prototype classification
Been Kim, Cynthia Rudin, and Julie A Shah · 2014
Cited alongside, same era.
Vqa: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi Parikh · 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.
Explaining nonlinear classification decisions with deep taylor decomposition
Grégoire Montavon, Sebastian Bach, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller · 2015
Later among the works it cites.
Interpreting visual question answering models
Yash Goyal, Akrit Mohapatra, Devi Parikh, and Dhruv Batra · 2016
Closest in time.
Interpretable decision sets: A joint framework for description and prediction
Himabindu Lakkaraju, Stephen H. Bach, and Jure Leskovec · 2016
Closest in time.
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