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Counterfactual explanations (CFE) are being widely used to explain algorithmic decisions, especially in consequential decision-making contexts (e.g., loan approval or pretrial bail).
Algorithms for Verifying Deep Neural Networks
Liu, C.; Arnon, T.; Lazarus, C.; Barrett, C. W.; and Kochenderfer, M. J. 2019 · 1903
Earlier work this paper cites.
https://archive.ics.uci.edu/ml/datasets/adult
Adult data. 1996 · 1996
Earlier work this paper cites.
Interval Arithmetic: From Principles to Implementation
Hickey, T.; Ju, Q.; and Van Emden, M. H. 2001 · 2001
Earlier work this paper cites.
Z3: An Efficient SMT Solver
de Moura, L.; and Bjørner, N. 2008 · 2008
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Fast Intersection Algorithms for Sorted Sequences , 45–61
Baeza-Yates, R.; and Salinger, A. 2010 · 2010
Earlier work this paper cites.
UCI machine learning repository
Bache, K.; and Lichman, M. 2013 · 2013
Earlier work this paper cites.
Deep learning
LeCun, Y.; Bengio, Y.; and Hinton, G. 2015 · 2015
Earlier work this paper cites.
https://github.com/propublica/compas-analysis
Larson, J.; Mattu, S.; Kirchner, L.; and Angwin, J. 2016 · 2016
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
Carlini, N.; and Wagner, D. 2017 · 2017
Earlier work this paper cites.
Towards a rigorous science of interpretable machine learning
Doshi-Velez, F.; and Kim, B. 2017 · 2017
Cited alongside, same era.
Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks
Ehlers, R. 2017 · 2017
Cited alongside, same era.
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Katz, G.; Barrett, C.; Dill, D. L.; Julian, K.; and Kochenderfer, M. J. 2017 · 2017
Cited alongside, same era.
Universal adversarial perturbations
Moosavi-Dezfooli, S.-M.; Fawzi, A.; Fawzi, O.; and Frossard, P. 2017 · 2017
Cited alongside, same era.
Practical black-box attacks against machine learning
Papernot, N.; McDaniel, P.; Goodfellow, I.; Jha, S.; Celik, Z. B.; and Swami, A. 2017 · 2017
Cited alongside, same era.
Efficient Search for Diverse Coherent Explanations
Russell, C. 2019 · 2019
Later among the works it cites.
Actionable Recourse in Linear Classification
Ustun, B.; Spangher, A.; and Liu, Y. 2019 · 2019
Later among the works it cites.
Counterfactual Explanations & Adversarial Examples – Common Grounds, Essential Differences, and Potential Transfers
Freiesleben, T. 2020 · 2020
Closest in time.
Gurobi Optimizer Reference Manual
Gurobi Optimization, L. 2020 · 2020
Closest in time.
DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization
Kanamori, K.; Takagi, T.; Kobayashi, K.; and Arimura, H. 2020 · 2020
Closest in time.
Algorithmic Recourse: from Counterfactual Explanations to Interventions
Karimi, A.-H.; Schölkopf, B.; and Valera, I. 2020 · 2020
Closest in time.
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Tjeng, V.; and Tedrake, R. 2017 · 2017
Cited alongside, same era.
Counterfactual explanations without opening the black box: Automated decisions and the GDPR
Wachter, S.; Mittelstadt, B.; and Russell, C. 2017 · 2017
Cited alongside, same era.
A Unified View of Piecewise Linear Neural Network Verification
Bunel, R.; Turkaslan, I.; Torr, P. H.; Kohli, P.; and Kumar, M. P. 2018 · 2018
Cited alongside, same era.
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
Rudin, C. 2018 · 2018
Cited alongside, same era.
Model-Agnostic Counterfactual Explanations for Consequential Decisions
Karimi, A.-H.; Barthe, G.; Balle, B.; and Valera, I. 2020a
Cited in the paper.
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Karimi, A.-H.; Barthe, G.; Schölkopf, B.; and Valera, I. 2020b
Cited in the paper.
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Mothilal, R. K.; Sharma, A.; and Tan, C. 2020 · 2020
Closest in time.
Counterfactual Explanations for Machine Learning: A Review
Verma, S.; Dickerson, J.; and Hines, K. 2020 · 2020
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