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Formal XAI (explainable AI) is a growing area that focuses on computing explanations with mathematical guarantees for the decisions made by ML models.
Branching Programs and Binary Decision Diagrams
I. Wegener · 2000
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Bdds–design, analysis, complexity, and applications
I. Wegener · 2004
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Complexity of DNF and isomorphism of monotone formulas
J. Goldsmith, M. Hagen, and M. Mundhenk · 2005
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Towards an optimal CNF encoding of boolean cardinality constraints
C. Sinz · 2005
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Handbook of Satisfiability: Volume 185 Frontiers in Artificial Intelligence and Applications
A. Biere, A. Biere, M. Heule, H. van Maaren, and T. Walsh · 2009
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Y. Izza, A. Ignatiev, and J. Marques-Silva · 2010
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On explaining decision trees, 2020b
Y. Izza, A. Ignatiev, and J. Marques-Silva · 2010
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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The mnist database of handwritten digit images for machine learning research
L. Deng · 2012
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Compiling neural networks into tractable boolean circuits
A. Choi, W. Shi, A. Shih, and A. Darwiche · 2017
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A unified approach to interpreting model predictions
S. M. Lundberg and S.-I. Lee · 2017
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Explaining explanations: An overview of interpretability of machine learning
L. H. Gilpin, D. Bau, B. Z. Yuan, A. Bajwa, M. A. Specter, and L. Kagal · 2018
Cited alongside, same era.
The mythos of model interpretability
Z. C. Lipton · 2018
Cited alongside, same era.
Anchors: High-precision model-agnostic explanations
M. T. Ribeiro, S. Singh, and C. Guestrin · 2018
Cited alongside, same era.
A symbolic approach to explaining bayesian network classifiers
A. Shih, A. Choi, and A. Darwiche · 2018
Cited alongside, same era.
Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
A. B. Arrieta, N. Díaz-Rodríguez, J. D. Ser, A. Bennetot, S. Tabik, A. Barbado, S. Garcia, S. Gil-Lopez, D. Molina, R. Benjamins, R. Chatila, and F. Herrera · 2019
Cited alongside, same era.
On efficiently explaining graph-based classifiers
X. Huang, Y. Izza, A. Ignatiev, and J. Marques-Silva · 2021
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SAT-based rigorous explanations for decision lists
A. Ignatiev and J. Marques-Silva · 2021
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Efficient explanations with relevant sets
Y. Izza, A. Ignatiev, N. Narodytska, M. C. Cooper, and J. Marques-Silva · 2021
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Explanations for monotonic classifiers
J. Marques-Silva, T. Gerspacher, M. C. Cooper, A. Ignatiev, and N. Narodytska · 2021
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Explanations for monotonic classifiers
J. Marques-Silva, T. Gerspacher, M. C. Cooper, A. Ignatiev, and N. Narodytska · 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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Model interpretability through the lens of computational complexity
P. Barceló, M. Monet, J. Pérez, and B. Subercaseaux · 2020
Cited alongside, same era.
CaDiCaL, Kissat, Paracooba, Plingeling and Treengeling entering the SAT Competition 2020
A. Biere, K. Fazekas, M. Fleury, and M. Heisinger · 2020
Cited alongside, same era.
Explaining naive bayes and other linear classifiers with polynomial time and delay
J. Marques-Silva, T. Gerspacher, M. C. Cooper, A. Ignatiev, and N. Narodytska · 2020
Cited alongside, same era.
Foundations of symbolic languages for model interpretability
M. Arenas, D. Báez, P. Barceló, J. Pérez, and B. Subercaseaux · 2021
Cited alongside, same era.
On the computational intelligibility of boolean classifiers
G. Audemard, S. Bellart, L. Bounia, F. Koriche, J. Lagniez, and P. Marquis · 2021
Cited alongside, same era.
Provably efficient, succinct, and precise explanations
G. Blanc, J. Lange, and L.-Y. Tan · 2021
Cited alongside, same era.
On the explanatory power of decision trees, 2021b
G. Audemard, S. Bellart, L. Bounia, F. Koriche, J.-M. Lagniez, and P. Marquis
Cited in the paper.
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Probabilistic sufficient explanations
E. Wang, P. Khosravi, and G. V. den Broeck · 2021
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If you like shapley then you’ll love the core
T. Yan and A. D. Procaccia · 2021
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Using maxsat for efficient explanations of tree ensembles
A. Ignatiev, Y. Izza, P. J. Stuckey, and J. Marques-Silva · 2022
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Provably precise, succinct and efficient explanations for decision trees, 2022
Y. Izza, A. Ignatiev, N. Narodytska, M. C. Cooper, and J. Marques-Silva · 2022
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Delivering trustworthy ai through formal xai
J. Marques-Silva and A. Ignatiev · 2022
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