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The rise of AI methods to make predictions and decisions has led to a pressing need for more explainable artificial intelligence (XAI) methods.
On Validating, Repairing and Refining Heuristic ML Explanations
Ignatiev, A.; Narodytska, N.; and Marques-Silva, J. 2019c · 1907
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Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods
Camburu, O.; Giunchiglia, E.; Foerster, J.; Lukasiewicz, T.; and Blunsom, P. 2019 · 1910
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Literal-Based MCS Extraction
Mencia, C.; Previti, A.; and Marques-Silva, J. 2015 · 1979
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A Theory of Diagnosis from First Principles
Reiter, R. 1987 · 1987
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Learning Decision Lists
Rivest, R. L. 1987 · 1987
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The CN2 Induction Algorithm
Clark, P.; and Niblett, T. 1989 · 1989
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A continuous approach to inductive inference
Kamath, A. P.; Karmarkar, N.; Ramakrishnan, K. G.; and Resende, M. G. C. 1992 · 1992
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Fast algorithms for mining association rules
Agrawal, R.; and Srikant, R. 1994 · 1994
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Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid
Kohavi, R. 1996 · 1996
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New Algorithms for Fast Discovery of Association Rules
Zaki, M. J.; Parthasarathy, S.; Ogihara, M.; and Li, W. 1997 · 1997
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Algorithms for Association Rule Mining - A General Survey and Comparison
Hipp, J.; Güntzer, U.; and Nakhaeizadeh, G. 2000 · 2000
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Greedy Function Approximation: A Gradient Boosting Machine
Friedman, J. H. 2001 · 2001
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Association Rule Mining, Models and Algorithms
Zhang, C.; and Zhang, S. 2002 · 2002
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Discovery of Minimal Unsatisfiable Subsets of Constraints Using Hitting Set Dualization
Bailey, J.; and Stuckey, P. J. 2005 · 2005
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Algorithms for Computing Minimal Unsatisfiable Subsets of Constraints
Liffiton, M. H.; and Sakallah, K. A. 2008 · 2008
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Towards efficient MUS extraction
Belov, A.; Lynce, I.; and Marques-Silva, J. 2012 · 2012
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On Computing Minimal Correction Subsets
Marques-Silva, J.; Heras, F.; Janota, M.; Previti, A.; and Belov, A. 2013 · 2013
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Intriguing properties of neural networks
Szegedy, C.; Zaremba, W.; Sutskever, I.; Bruna, J.; Erhan, D.; Goodfellow, I. J.; and Fergus, R. 2014 · 2014
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PySMT: a Solver-Agnostic Library for Fast Prototyping of SMT-Based Algorithms
Gario, M.; and Micheli, A. 2015 · 2015
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Smallest MUS Extraction with Minimal Hitting Set Dualization
Ignatiev, A.; Previti, A.; Liffiton, M. H.; and Marques-Silva, J. 2015 · 2015
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Deep learning
LeCun, Y.; Bengio, Y.; and Hinton, G. 2015 · 2015
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Machine Bias
Angwin, J.; Larson, J.; Mattu, S.; and Kirchner, L. 2016 · 2016
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XGBoost: A Scalable Tree Boosting System
Chen, T.; and Guestrin, C. 2016 · 2016
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Binarized Neural Networks
Hubara, I.; Courbariaux, M.; Soudry, D.; El-Yaniv, R.; and Bengio, Y. 2016 · 2016
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Interpretable Decision Sets: A Joint Framework for Description and Prediction
Lakkaraju, H.; Bach, S. H.; and Leskovec, J. 2016 · 2016
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Fast, flexible MUS enumeration
Liffiton, M. H.; Previti, A.; Malik, A.; and Marques-Silva, J. 2016 · 2016
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”Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Ribeiro, M. T.; Singh, S.; and Guestrin, C. 2016 · 2016
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UCI Machine Learning Repository
Dua, D.; and Graff, C. 2017 · 2017
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A Unified Approach to Interpreting Model Predictions
Lundberg, S. M.; and Lee, S. 2017 · 2017
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PMLB: a large benchmark suite for machine learning evaluation and comparison
Olson, R. S.; Cava, W. G. L.; Orzechowski, P.; Urbanowicz, R. J.; and Moore, J. H. 2017 · 2017
Cited alongside, same era.
Fathers of the Deep Learning Revolution Receive ACM A.M. Turing Award
ACM. 2018 · 2018
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Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay
Marques-Silva, J.; Gerspacher, T.; Cooper, M. C.; Ignatiev, A.; and Narodytska, N. 2020 · 2020
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Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods
Slack, D.; Hilgard, S.; Jia, E.; Singh, S.; and Lakkaraju, H. 2020 · 2020
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Computing Optimal Decision Sets with SAT
Yu, J.; Ignatiev, A.; Stuckey, P. J.; and Bodic, P. L. 2020 · 2020
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Foundations of Symbolic Languages for Model Interpretability
Arenas, M.; Baez, D.; Barceló, P.; Pérez, J.; and Subercaseaux, B. 2021 · 2021
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Handbook of Satisfiability . IOS Press
Biere, A.; Heule, M.; van Maaren, H.; and Walsh, T., eds. 2021 · 2021
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Provably efficient, succinct, and precise explanations
Blanc, G.; Lange, J.; and Tan, L. 2021 · 2021
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Recursive Online Enumeration of All Minimal Unsatisfiable Subsets
Bendík, J.; Cerná, I.; and Benes, N. 2018 · 2018
Cited alongside, same era.
PySAT: A Python Toolkit for Prototyping with SAT Oracles
Ignatiev, A.; Morgado, A.; and Marques-Silva, J. 2018 · 2018
Cited alongside, same era.
A SAT-Based Approach to Learn Explainable Decision Sets
Ignatiev, A.; Pereira, F.; Narodytska, N.; and Marques-Silva, J. 2018 · 2018
Cited alongside, same era.
The mythos of model interpretability
Lipton, Z. C. 2018 · 2018
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MLIC: A MaxSAT-Based Framework for Learning Interpretable Classification Rules
Malioutov, D.; and Meel, K. S. 2018 · 2018
Cited alongside, same era.
Anchors: High-Precision Model-Agnostic Explanations
Ribeiro, M. T.; Singh, S.; and Guestrin, C. 2018 · 2018
Cited alongside, same era.
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ASTERYX: A model-Agnostic SaT-basEd appRoach for sYmbolic and score-based eXplanations
Boumazouza, R.; Alili, F. C.; Mazure, B.; and Tabia, K. 2021 · 2021
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On Quantifying Literals in Boolean Logic and Its Applications to Explainable AI
Darwiche, A.; and Marquis, P. 2021 · 2021
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A Scalable Two Stage Approach to Computing Optimal Decision Sets
Ignatiev, A.; Lam, E.; Stuckey, P. J.; and Marques-Silva, J. 2021 · 2021
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SAT-Based Rigorous Explanations for Decision Lists
Ignatiev, A.; and Marques-Silva, J. 2021 · 2021
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On Explaining Random Forests with SAT
Izza, Y.; and Marques-Silva, J. 2021 · 2021
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On Guaranteed Optimal Robust Explanations for NLP Models
Malfa, E. L.; Michelmore, R.; Zbrzezny, A. M.; Paoletti, N.; and Kwiatkowska, M. 2021 · 2021
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Explanations for Monotonic Classifiers
Marques-Silva, J.; Gerspacher, T.; Cooper, M. C.; Ignatiev, A.; and Narodytska, N. 2021 · 2021
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The Computational Complexity of Understanding Binary Classifier Decisions
Wäldchen, S.; MacDonald, J.; Hauch, S.; and Kutyniok, G. 2021 · 2021
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Learning Optimal Decision Sets and Lists with SAT
Yu, J.; Ignatiev, A.; Stuckey, P. J.; and Le Bodic, P. 2021 · 2021
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Axiomatic Foundations of Explainability
Amgoud, L.; and Ben-Naim, J. 2022 · 2022
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On Computing Probabilistic Explanations for Decision Trees
Arenas, M.; Barceló, P.; Romero, M.; and Subercaseaux, B. 2022 · 2022
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Looking Inside the Black-Box: Logic-based Explanations for Neural Networks
Ferreira, J.; de Sousa Ribeiro, M.; Gonçalves, R.; and Leite, J. 2022 · 2022
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Sufficient Reasons for Classifier Decisions in the Presence of Domain Constraints
Gorji, N.; and Rubin, S. 2022 · 2022
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Tractable Explanations for d-DNNF Classifiers
Huang, X.; Izza, Y.; Ignatiev, A.; Cooper, M. C.; Asher, N.; and Marques-Silva, J. 2022 · 2022
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Using MaxSAT for Efficient Explanations of Tree Ensembles
Ignatiev, A.; Izza, Y.; Stuckey, P. J.; and Marques-Silva, J. 2022 · 2022
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On Tackling Explanation Redundancy in Decision Trees
Izza, Y.; Ignatiev, A.; and Marques-Silva, J. 2022 · 2022
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Delivering Trustworthy AI through Formal XAI
Marques-Silva, J.; and Ignatiev, A. 2022 · 2022
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Constraint-Driven Explanations of Black-Box ML Models
Shrotri, A.; Narodytska, N.; Ignatiev, A.; Meel, K. S.; Marques-Silva, J.; and Vardi, M. 2022 · 2022
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