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Leveraging Integer Linear Programming to Learn Optimal Fair Rule Lists.

, , , , and . CPAIOR, volume 13292 of Lecture Notes in Computer Science, page 103-119. Springer, (2022)

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Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule Lists., , , , and . CoRR, (2024)Probabilistic Dataset Reconstruction from Interpretable Models., , , , and . SaTML, page 1-17. IEEE, (2024)FairCORELS, an Open-Source Library for Learning Fair Rule Lists., , , , and . CIKM, page 4665-4669. ACM, (2021)Exploiting Fairness to Enhance Sensitive Attributes Reconstruction., , , , and . CoRR, (2022)Learning Optimal Fair Scoring Systems for Multi-Class Classification., , and . ICTAI, page 197-204. IEEE, (2022)Improving fairness generalization through a sample-robust optimization method., , , , and . Mach. Learn., 112 (6): 2131-2192 (June 2023)Trained Random Forests Completely Reveal your Dataset., , , and . ICML, OpenReview.net, (2024)Leveraging Integer Linear Programming to Learn Optimal Fair Rule Lists., , , , and . CPAIOR, volume 13292 of Lecture Notes in Computer Science, page 103-119. Springer, (2022)Addressing Interpretability, Fairness and Privacy in Machine Learning Through Combinatorial Optimization Methods. (Adresser l'interprétabilité, l'équité et la protection de la vie privée en apprentissage machine au travers des méthodes d'optimisation combinatoire).. Paul Sabatier University, Toulouse, France, (2023)Exploiting Fairness to Enhance Sensitive Attributes Reconstruction., , , , and . SaTML, page 18-41. IEEE, (2023)