Learning Fair Rule Lists. - Ulrich Aïvodji; Julien Ferry; Sébastien Gambs; Marie-José Huguet; Mohamed Siala 002 (CoRR, 2019)

FairCORELS, an Open-Source Library for Learning Fair Rule Lists. - Ulrich Aïvodji; Julien Ferry; Sébastien Gambs; Marie-José Huguet; Mohamed Siala 002 (CIKM, 2021)

Leveraging Integer Linear Programming to Learn Optimal Fair Rule Lists. - Ulrich Aïvodji; Julien Ferry; Sébastien Gambs; Marie-José Huguet; Mohamed Siala 002 (CPAIOR, 2022)

Learning Optimal Fair Scoring Systems for Multi-Class Classification. - Julien Rouzot; Julien Ferry; Marie-José Huguet (ICTAI, 2022)

Exploiting Fairness to Enhance Sensitive Attributes Reconstruction. - Julien Ferry; Ulrich Aïvodji; Sébastien Gambs; Marie-José Huguet; Mohamed Siala 002 (SaTML, 2023)

SoK: Taming the Triangle - On the Interplays between Fairness, Interpretability and Privacy in Machine Learning. - Julien Ferry; Ulrich Aïvodji; Sébastien Gambs; Marie-José Huguet; Mohamed Siala 002 (CoRR, 2023)

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). - Julien Ferry (2023)

Improving fairness generalization through a sample-robust optimization method. - Julien Ferry; Ulrich Aïvodji; Sébastien Gambs; Marie-José Huguet; Mohamed Siala 002 (Mach. Learn., 2023)

Trained Random Forests Completely Reveal your Dataset. - Julien Ferry; Ricardo Fukasawa; Timothée Pascal; Thibaut Vidal (ICML, 2024)

Probabilistic Dataset Reconstruction from Interpretable Models. - Julien Ferry; Ulrich Aïvodji; Sébastien Gambs; Marie-José Huguet; Mohamed Siala 002 (SaTML, 2024)

Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule Lists. - Timothée Ly; Julien Ferry; Marie-José Huguet; Sébastien Gambs; Ulrich Aïvodji (CoRR, 2024)

Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods. - Julien Ferry; Gabriel Laberge; Ulrich Aïvodji (Trans. Mach. Learn. Res., 2024)

Fairness and Sparsity within Rashomon sets: Enumeration-Free Exploration and Characterization. - Lucas Langlade; Julien Ferry; Gabriel Laberge; Thibaut Vidal (CoRR, 2025)

Training Set Reconstruction from Differentially Private Forests: How Effective is DP? - Alice Gorgé; Julien Ferry; Sébastien Gambs; Thibaut Vidal (CoRR, 2025)

From Counterfactuals to Trees: Competitive Analysis of Model Extraction Attacks. - Awa Khouna; Julien Ferry; Thibaut Vidal (CoRR, 2025)

Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods. - Fabian Akkerman; Julien Ferry; Christian Artigues; Emmanuel Hebrard; Thibaut Vidal (CoRR, 2025)