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).

[BibT_eX]

[DOI]

Julien Ferry

PhD thesis, 2023

SoK: Taming the Triangle - On the Interplays between Fairness, Interpretability and Privacy in Machine Learning.

[BibT_eX]

[DOI]

CoRR, 2023

Probabilistic Dataset Reconstruction from Interpretable Models.

[BibT_eX]

[DOI]

CoRR, 2023

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

[BibT_eX]

[DOI]

Julien Ferry

Gabriel Laberge

Ulrich Aïvodji

CoRR, 2023

2022

Exploiting Fairness to Enhance Sensitive Attributes Reconstruction.

[BibT_eX]

[DOI]

CoRR, 2022

Learning Optimal Fair Scoring Systems for Multi-Class Classification.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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

2021

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

CoRR, 2019

Julien Ferry

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