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Recent work proposed $\delta$-relevant inputs (or sets) as a probabilistic explanation for the predictions made by a classifier on a given input.
The magical number seven, plus or minus two: Some limits on our capacity for processing information
G. A. Miller · 1956
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R. Reiter · 1987
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Locating minimal infeasible constraint sets in linear programs
J. W. Chinneck and E. W. Dravnieks · 1991
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Computational complexity
C. H. Papadimitriou · 1994
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Decision tree induction based on efficient tree restructuring
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Statistical modeling: The two cultures
L. Breiman · 2001
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A knowledge compilation map
A. Darwiche and P. Marquis · 2002
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Set-theoretic duality: A fundamental feature of combinatorial optimisation
J. Slaney · 2014
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ν \nu z - an optimizing SMT solver
N. Bjørner, A. Phan, and L. Fleckenstein · 2015
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"Why should I trust you?": Explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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A unified approach to interpreting model predictions
S. M. Lundberg and S. Lee · 2017
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PMLB: a large benchmark suite for machine learning evaluation and comparison
R. S. Olson, W. La Cava, P. Orzechowski, R. J. Urbanowicz, and J. H. Moore · 2017
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Satisfiability modulo theories
C. W. Barrett and C. Tinelli · 2018
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The mythos of model interpretability
Z. C. Lipton · 2018
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Methods for interpreting and understanding deep neural networks
G. Montavon, W. Samek, and K. Müller · 2018
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Anchors: High-precision model-agnostic explanations
M. T. Ribeiro, S. Singh, and C. Guestrin · 2018
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A symbolic approach to explaining bayesian network classifiers
A. Shih, A. Choi, and A. Darwiche · 2018
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Can I trust the explainer? verifying post-hoc explanatory methods
O. Camburu, E. Giunchiglia, J. Foerster, T. Lukasiewicz, and P. Blunsom · 2019
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A survey of methods for explaining black box models
R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, and D. Pedreschi · 2019
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Abduction-based explanations for machine learning models
On tractable XAI queries based on compiled representations
G. Audemard, F. Koriche, and P. Marquis · 2020
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Model interpretability through the lens of computational complexity
P. Barceló, M. Monet, J. Pérez, and B. Subercaseaux · 2020
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On the reasons behind decisions
A. Darwiche and A. Hirth · 2020
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From contrastive to abductive explanations and back again
A. Ignatiev, N. Narodytska, N. Asher, and J. Marques-Silva · 2020
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https://www-lrn.cs.umass.edu/iti/ , 2020
Incremental Decision Tree Induction · 2020
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Y. Izza, A. Ignatiev, and J. Marques-Silva · 2020
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A. Ignatiev, N. Narodytska, and J. Marques-Silva · 2019
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On relating explanations and adversarial examples
A. Ignatiev, N. Narodytska, and J. Marques-Silva · 2019
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What to expect of classifiers? reasoning about logistic regression with missing features
P. Khosravi, Y. Liang, Y. Choi, and G. Van den Broeck · 2019
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Explanation in artificial intelligence: Insights from the social sciences
T. Miller · 2019
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Assessing heuristic machine learning explanations with model counting
N. Narodytska, A. A. Shrotri, K. S. Meel, A. Ignatiev, and J. Marques-Silva · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
C. Rudin · 2019
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Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, and K. Müller, editors · 2019
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"how do I fool you?": Manipulating user trust via misleading black box explanations
H. Lakkaraju and O. Bastani · 2020
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Interpretable Machine Learning
C. Molnar · 2020
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Fooling LIME and SHAP: adversarial attacks on post hoc explanation methods
D. Slack, S. Hilgard, E. Jia, S. Singh, and H. Lakkaraju · 2020
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https://archive.ics.uci.edu/ml , 2020
UCI Machine Learning Repository · 2020
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On the computational intelligibility of boolean classifiers
G. Audemard, S. Bellart, L. Bounia, F. Koriche, J. Lagniez, and P. Marquis · 2021
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On guaranteed optimal robust explanations for NLP models
E. L. Malfa, A. Zbrzezny, R. Michelmore, N. Paoletti, and M. Kwiatkowska · 2021
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Deceiving AI
D. Monroe · 2021
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The computational complexity of understanding binary classifier decisions
S. Wäldchen, J. MacDonald, S. Hauch, and G. Kutyniok · 2021
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