Julien Ferry

24 papers A* 1A 1B 2Journal 17Unranked 2
YearRankTypeTitle / Venue / Authors
2026 J jnl
Counterfactual Maps: What They Are and How to Find Them.
CoRR
Awa Khouna, Julien Ferry, Thibaut Vidal
2025 J jnl
Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods.
CoRR
Fabian Akkerman, Julien Ferry, Christian Artigues, Emmanuel Hebrard, Thibaut Vidal
2025 J jnl
Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods.
Trans. Mach. Learn. Res.
Fabian Akkerman, Julien Ferry, Christian Artigues, Emmanuel Hebrard, Thibaut Vidal
2025 J jnl
Fairness and Sparsity within Rashomon sets: Enumeration-Free Exploration and Characterization.
CoRR
Lucas Langlade, Julien Ferry, Gabriel Laberge, Thibaut Vidal
2025 J jnl
From Counterfactuals to Trees: Competitive Analysis of Model Extraction Attacks.
CoRR
Awa Khouna, Julien Ferry, Thibaut Vidal
2025 J jnl
Taming the Triangle: On the Interplays Between Fairness, Interpretability, and Privacy in Machine Learning.
Comput. Intell.
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala
2025 J jnl
Training Set Reconstruction from Differentially Private Forests: How Effective is DP?
CoRR
Alice Gorgé, Julien Ferry, Sébastien Gambs, Thibaut Vidal
2024 J jnl
Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods.
Trans. Mach. Learn. Res.
Julien Ferry, Gabriel Laberge, Ulrich Aïvodji
2024 — conf
Probabilistic Dataset Reconstruction from Interpretable Models.
SaTML
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala
2024 J jnl
Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule Lists.
CoRR
Timothée Ly, Julien Ferry, Marie-José Huguet, Sébastien Gambs, Ulrich Aïvodji
2024 A* conf
Trained Random Forests Completely Reveal your Dataset.
ICML
Julien Ferry, Ricardo Fukasawa, Timothée Pascal, Thibaut Vidal
2024 J jnl
Trained Random Forests Completely Reveal your Dataset.
CoRR
Julien Ferry, Ricardo Fukasawa, Timothée Pascal, Thibaut Vidal
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 — conf
Exploiting Fairness to Enhance Sensitive Attributes Reconstruction.
SaTML
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala
2023 J jnl
Improving fairness generalization through a sample-robust optimization method.
Mach. Learn.
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala
2023 J jnl
Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods.
CoRR
Julien Ferry, Gabriel Laberge, Ulrich Aïvodji
2023 J jnl
Learning Optimal Fair Scoring Systems for Multi-Class Classification.
CoRR
Julien Rouzot, Julien Ferry, Marie-José Huguet
2023 J jnl
Probabilistic Dataset Reconstruction from Interpretable Models.
CoRR
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala
2023 J jnl
SoK: Taming the Triangle - On the Interplays between Fairness, Interpretability and Privacy in Machine Learning.
CoRR
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala
2022 J jnl
Exploiting Fairness to Enhance Sensitive Attributes Reconstruction.
CoRR
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala
2022 B conf
Learning Optimal Fair Scoring Systems for Multi-Class Classification.
ICTAI
Julien Rouzot, Julien Ferry, Marie-José Huguet
2022 B conf
Leveraging Integer Linear Programming to Learn Optimal Fair Rule Lists.
CPAIOR
Ulrich Aïvodji, Julien Ferry, Sébastien Gambs, Marie-José Huguet, Mohamed Siala
2021 A conf
FairCORELS, an Open-Source Library for Learning Fair Rule Lists.
CIKM
Ulrich Aïvodji, Julien Ferry, Sébastien Gambs, Marie-José Huguet, Mohamed Siala
2019 J jnl
Learning Fair Rule Lists.
CoRR
Ulrich Aïvodji, Julien Ferry, Sébastien Gambs, Marie-José Huguet, Mohamed Siala