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[1] Ailsa H. Land,et al. An Automatic Method of Solving Discrete Programming Problems , 1960 .
[2] R. Gomory,et al. A Linear Programming Approach to the Cutting-Stock Problem , 1961 .
[3] Margo I. Seltzer,et al. Scalable Bayesian Rule Lists , 2016, ICML.
[4] Toon Calders,et al. Three naive Bayes approaches for discrimination-free classification , 2010, Data Mining and Knowledge Discovery.
[5] Cynthia Rudin,et al. A Bayesian Framework for Learning Rule Sets for Interpretable Classification , 2017, J. Mach. Learn. Res..
[6] Martin W. P. Savelsbergh,et al. Branch-and-Price: Column Generation for Solving Huge Integer Programs , 1998, Oper. Res..
[7] Cynthia Rudin,et al. Falling Rule Lists , 2014, AISTATS.
[8] Krishna P. Gummadi,et al. Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment , 2016, WWW.
[9] Sharad Goel,et al. The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning , 2018, ArXiv.
[10] Jon M. Kleinberg,et al. Inherent Trade-Offs in the Fair Determination of Risk Scores , 2016, ITCS.
[11] Jure Leskovec,et al. Interpretable Decision Sets: A Joint Framework for Description and Prediction , 2016, KDD.
[12] Sanjeeb Dash,et al. Boolean Decision Rules via Column Generation , 2018, NeurIPS.
[13] Alexandra Chouldechova,et al. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments , 2016, Big Data.
[14] Nathan Srebro,et al. Equality of Opportunity in Supervised Learning , 2016, NIPS.
[15] Cynthia Rudin,et al. Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model , 2015, ArXiv.
[17] Kristina Lerman,et al. A Survey on Bias and Fairness in Machine Learning , 2019, ACM Comput. Surv..
[18] Amos J. Storkey,et al. Censoring Representations with an Adversary , 2015, ICLR.
[19] S. Ilker Birbil,et al. Rule Covering for Interpretation and Boosting , 2020, ArXiv.
[20] Phebe Vayanos,et al. Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making , 2019, AAAI.
[21] Cynthia Rudin,et al. Learning Cost-Effective and Interpretable Treatment Regimes , 2017, AISTATS.
[22] Seth Neel,et al. A Convex Framework for Fair Regression , 2017, ArXiv.
[23] Ronald L. Rivest,et al. Learning decision lists , 2004, Machine Learning.
[24] Lu Zhang,et al. Fairness-aware Classification: Criterion, Convexity, and Bounds , 2018, ArXiv.
[25] Krishna P. Gummadi,et al. Fairness Constraints: Mechanisms for Fair Classification , 2015, AISTATS.
[26] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[27] Oktay Günlük,et al. Optimal Generalized Decision Trees via Integer Programming , 2016, ArXiv.
[28] Ulrike von Luxburg,et al. Too Relaxed to Be Fair , 2020, ICML.
[29] Margo I. Seltzer,et al. Learning Certifiably Optimal Rule Lists , 2017, KDD.
[30] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[31] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[32] Tong Wang,et al. Learning Optimized Or's of And's , 2015, ArXiv.
[33] G. Nemhauser,et al. Integer Programming , 2020 .
[34] John Langford,et al. A Reductions Approach to Fair Classification , 2018, ICML.
[35] Shai Ben-David,et al. Empirical Risk Minimization under Fairness Constraints , 2018, NeurIPS.
[36] Dimitris Bertsimas,et al. Optimal classification trees , 2017, Machine Learning.
[37] Alberto Maria Segre,et al. Programs for Machine Learning , 1994 .
[38] Kush R. Varshney,et al. Learning sparse two-level boolean rules , 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).
[39] Toon Calders,et al. Discrimination Aware Decision Tree Learning , 2010, 2010 IEEE International Conference on Data Mining.
[40] Christopher T. Lowenkamp,et al. False Positives, False Negatives, and False Analyses: A Rejoinder to "Machine Bias: There's Software Used across the Country to Predict Future Criminals. and It's Biased against Blacks" , 2016 .
[41] Cynthia Rudin,et al. Supersparse Linear Integer Models for Interpretable Classification , 2013, 1306.6677.
[42] Cynthia Rudin,et al. Learning Optimized Risk Scores , 2016, J. Mach. Learn. Res..
[43] Faisal Kamiran,et al. Quantifying explainable discrimination and removing illegal discrimination in automated decision making , 2012, Knowledge and Information Systems.
[44] Toniann Pitassi,et al. Fairness through awareness , 2011, ITCS '12.