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Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models.
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Boolean Functions - Theory, Algorithms, and Applications
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Scikit-learn: Machine learning in python
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Towards efficient MUS extraction
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Decision trees: a recent overview
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On computing minimal correction subsets
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Minimal sets over monotone predicates in boolean formulae
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Understanding Machine Learning - From Theory to Algorithms
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Deep inside convolutional networks: Visualising image classification models and saliency maps
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Set-theoretic duality: A fundamental feature of combinatorial optimisation
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Efficient MUS enumeration of horn formulae with applications to axiom pinpointing
M. F. Arif, C. Mencía, and J. Marques-Silva · 2015
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek · 2015
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Literal-based MCS extraction
C. Mencía, A. Previti, and J. Marques-Silva · 2015
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Machine Learning: The New AI
E. Alpaydin · 2016
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Interpretable decision sets: A joint framework for description and prediction
H. Lakkaraju, S. H. Bach, and J. Leskovec · 2016
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Fast, flexible MUS enumeration
M. H. Liffiton, A. Previti, A. Malik, and J. Marques-Silva · 2016
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Efficient reasoning for inconsistent Horn formulae
J. Marques-Silva, A. Ignatiev, C. Mencía, and R. Peñaloza · 2016
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MCS extraction with sublinear oracle queries
C. Mencía, A. Ignatiev, A. Previti, and J. Marques-Silva · 2016
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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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MediBoost: a patient stratification tool for interpretable decision making in the era of precision medicine
G. Valdes, J. M. Luna, E. Eaton, C. B. Simone, L. H. Ungar, and T. D. Solberg · 2016
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Optimal classification trees
D. Bertsimas and J. Dunn · 2017
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A unified approach to interpreting model predictions
S. M. Lundberg and S. Lee · 2017
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Minimal sets on propositional formulae. problems and reductions
J. Marques-Silva, M. Janota, and C. Mencía · 2017
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Artificial Intelligence - Foundations of Computational Agents
D. Poole and A. K. Mackworth · 2017
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Learning decision trees with flexible constraints and objectives using integer optimization
S. Verwer and Y. Zhang · 2017
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Data Mining
I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal · 2017
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SAT-based encodings for optimal decision trees with explicit paths
M. Janota and A. Morgado · 2020
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A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
A. Karimi, G. Barthe, B. Schölkopf, and I. Valera · 2020
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Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies
J. D. Kelleher, B. Mac Namee, and A. D’arcy · 2020
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Generalized and scalable optimal sparse decision trees
J. Lin, C. Zhong, D. Hu, C. Rudin, and M. I. Seltzer · 2020
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From local explanations to global understanding with explainable AI for trees
S. M. Lundberg, G. Erion, H. Chen, A. DeGrave, J. M. Prutkin, B. Nair, R. Katz, J. Himmelfarb, N. Bansal, and S.-I. Lee · 2020
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Peeking inside the black-box: A survey on explainable artificial intelligence (XAI)
A. Adadi and M. Berrada · 2018
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Applied informatics decision support tool for mortality predictions in patients with cancer
D. Bertsimas, J. Dunn, C. Pawlowski, J. Silberholz, A. Weinstein, Y. D. Zhuo, E. Chen, and A. A. Elfiky · 2018
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Surgical risk is not linear: derivation and validation of a novel, user-friendly, and machine-learning-based predictive optimal trees in emergency surgery risk (potter) calculator
D. Bertsimas, J. Dunn, G. C. Velmahos, and H. M. Kaafarani · 2018
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PySAT: A python toolkit for prototyping with SAT oracles
A. Ignatiev, A. Morgado, and J. Marques-Silva · 2018
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A SAT-based approach to learn explainable decision sets
A. Ignatiev, F. Pereira, N. Narodytska, and J. Marques-Silva · 2018
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The mythos of model interpretability
Z. C. Lipton · 2018
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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
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Reasoning about inconsistent formulas
J. Marques-Silva and C. Mencía · 2020
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Interpretable Machine Learning
C. Molnar · 2020
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Machine learning and natural language processing methods to identify ischemic stroke, acuity and location from radiology reports
C. J. Ong, A. Orfanoudaki, R. Zhang, F. P. M. Caprasse, M. Hutch, L. Ma, D. Fard, O. Balogun, M. I. Miller, M. Minnig, H. Saglam, B. Prescott, D. M. Greer, S. Smirnakis, and D. Bertsimas · 2020
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https://www.openml.org/ , 2020
OpenML: Machine learning, better, together · 2020
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Machine learning provides evidence that stroke risk is not linear: The non-linear framingham stroke risk score
A. Orfanoudaki, E. Chesley, C. Cadisch, B. Stein, A. Nouh, M. J. Alberts, and D. Bertsimas · 2020
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https://github.com/EpistasisLab/pmlb , 2020
Penn Machine Learning Benchmarks · 2020
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Argumentation as a framework for interactive explanations for recommendations
A. Rago, O. Cocarascu, C. Bechlivanidis, and F. Toni · 2020
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On tractable representations of binary neural networks
W. Shi, A. Shih, A. Darwiche, and A. Choi · 2020
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https://archive.ics.uci.edu/ml , 2020
UCI Machine Learning Repository · 2020
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The philosophical basis of algorithmic recourse
S. Venkatasubramanian and M. Alfano · 2020
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Learning optimal decision trees using constraint programming
H. Verhaeghe, S. Nijssen, G. Pesant, C. Quimper, and P. Schaus · 2020
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Learning optimal decision trees using constraint programming (extended abstract)
H. Verhaeghe, S. Nijssen, G. Pesant, C. Quimper, and P. Schaus · 2020
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Learning optimal decision trees using MaxSAT
J. Alos, C. Ansotegui, and E. Torres · 2021
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Foundations of symbolic languages for model interpretability
M. Arenas, D. Baez, P. Barceló, J. Pérez, and B. Subercaseaux · 2021
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Fair and adequate explanations
N. Asher, S. Paul, and C. Russell · 2021
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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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H. Bandi and D. Bertsimas · 2021
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Predicting inpatient flow at a major hospital using interpretable analytics
D. Bertsimas, J. Pauphilet, J. Stevens, and M. Tandon · 2021
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The voice of optimization
D. Bertsimas and B. Stellato · 2021
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Handbook of Satisfiability
A. Biere, M. Heule, H. van Maaren, and T. Walsh, editors · 2021
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Provably efficient, succinct, and precise explanations
G. Blanc, J. Lange, and L. Tan · 2021
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ASTERYX: A model-agnostic sat-based approach for symbolic and score-based explanations
R. Boumazouza, F. C. Alili, B. Mazure, and K. Tabia · 2021
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On the tractability of explaining decisions of classifiers
M. C. Cooper and J. Marques-Silva · 2021
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On quantifying literals in boolean logic and its applications to explainable AI
A. Darwiche and P. Marquis · 2021
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Optimal decision trees for nonlinear metrics
E. Demirovic and P. J. Stuckey · 2021
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Validation of the artificial intelligence-based predictive optimal trees in emergency surgery risk (potter) calculator in emergency general surgery and emergency laparotomy patients
M. W. El Hechi, L. R. Maurer, J. Levine, D. Zhuo, M. El Moheb, G. C. Velmahos, J. Dunn, D. Bertsimas, and H. M. Kaafarani · 2021
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Sufficient reasons for classifier decisions in the presence of constraints
N. Gorji and S. Rubin · 2021
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Efficient explanations for knowledge compilation languages
X. Huang, Y. Izza, A. Ignatiev, M. C. Cooper, N. Asher, and J. Marques-Silva · 2021
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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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Reasoning-based learning of interpretable ML models
A. Ignatiev, J. Marques-Silva, N. Narodytska, and P. J. Stuckey · 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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On explaining random forests with SAT
Y. Izza and J. Marques-Silva · 2021
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Algorithmic recourse: from counterfactual explanations to interventions
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A logic for binary classifiers and their explanation
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On guaranteed optimal robust explanations for NLP models
E. L. Malfa, R. Michelmore, A. M. Zbrzezny, N. Paoletti, and M. Kwiatkowska · 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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Trauma outcome predictor: An artificial intelligence interactive smartphone tool to predict outcomes in trauma patients
L. R. Maurer, D. Bertsimas, H. T. Bouardi, M. El Hechi, M. El Moheb, K. Giannoutsou, D. Zhuo, J. Dunn, G. C. Velmahos, and H. M. Kaafarani · 2021
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Parameterized complexity of small decision tree learning
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Argumentative explanations for interactive recommendations
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Interpretable machine learning: Fundamental principles and 10 grand challenges
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Explaining deep neural networks and beyond: A review of methods and applications
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SAT-based decision tree learning for large data sets
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SAT-based approach for learning optimal decision trees with non-binary features
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Class imbalance and cost-sensitive decision trees: A unified survey based on a core similarity
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A practical tutorial for decision tree induction: Evaluation measures for candidate splits and opportunities
V. A. Sosa-Hernández, R. Monroy, M. A. Medina-Pérez, O. Loyola-González, and F. Herrera · 2021
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A survey on explainable artificial intelligence (XAI): toward medical XAI
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The computational complexity of understanding binary classifier decisions
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Probabilistic Sufficient Explanations
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Machine Learning
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Murtree: Optimal decision trees via dynamic programming and search
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