Julien Ferry

According to our database, Julien Ferry authored at least 16 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods.

Fabian Akkerman, Julien Ferry, Christian Artigues, Emmanuel Hebrard, Thibaut Vidal

CoRR, July, 2025

From Counterfactuals to Trees: Competitive Analysis of Model Extraction Attacks.

Awa Khouna, Julien Ferry, Thibaut Vidal

CoRR, February, 2025

Training Set Reconstruction from Differentially Private Forests: How Effective is DP?

Alice Gorgé, Julien Ferry, Sébastien Gambs, Thibaut Vidal

CoRR, February, 2025

Fairness and Sparsity within Rashomon sets: Enumeration-Free Exploration and Characterization.

Lucas Langlade, Julien Ferry, Gabriel Laberge, Thibaut Vidal

CoRR, February, 2025

2024

Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods.

Julien Ferry, Gabriel Laberge, Ulrich Aïvodji

Trans. Mach. Learn. Res., 2024

Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule Lists.

Timothée Ly, Julien Ferry, Marie-José Huguet, Sébastien Gambs, Ulrich Aïvodji

CoRR, 2024

Probabilistic Dataset Reconstruction from Interpretable Models.

Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala

Proceedings of the IEEE Conference on Secure and Trustworthy Machine Learning, 2024

Trained Random Forests Completely Reveal your Dataset.

Julien Ferry, Ricardo Fukasawa, Timothée Pascal, Thibaut Vidal

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Improving fairness generalization through a sample-robust optimization method.

Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala

Mach. Learn., June, 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

PhD thesis, 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

CoRR, 2023

Exploiting Fairness to Enhance Sensitive Attributes Reconstruction.

Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala

Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning, 2023

2022

Learning Optimal Fair Scoring Systems for Multi-Class Classification.

Julien Rouzot, Julien Ferry, Marie-José Huguet

Proceedings of the 34th IEEE International Conference on Tools with Artificial Intelligence, 2022

Leveraging Integer Linear Programming to Learn Optimal Fair Rule Lists.

Ulrich Aïvodji, Julien Ferry, Sébastien Gambs, Marie-José Huguet, Mohamed Siala

Proceedings of the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 2022

2021

FairCORELS, an Open-Source Library for Learning Fair Rule Lists.

Ulrich Aïvodji, Julien Ferry, Sébastien Gambs, Marie-José Huguet, Mohamed Siala

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

2019

Learning Fair Rule Lists.

Ulrich Aïvodji, Julien Ferry, Sébastien Gambs, Marie-José Huguet, Mohamed Siala

CoRR, 2019