7 citations · 13 across the 7 of their papers we have counts for
5 papers · 1 filter
Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule Lists
Timothée Ly, Julien Ferry, Marie-José Huguet +2
Differentially-private (DP) mechanisms can be embedded into the design of a machine learning algorithm to protect the resulting model against privacy leakage. However, this often c…
SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs +2
Machine learning techniques are increasingly used for high-stakes decision-making, such as college admissions, loan attribution or recidivism prediction. Thus, it is crucial to ens…
Learning Optimal Fair Scoring Systems for Multi-Class Classification
Julien Rouzot, Julien Ferry, Marie-José Huguet
Machine Learning models are increasingly used for decision making, in particular in high-stakes applications such as credit scoring, medicine or recidivism prediction. However, the…
Exploiting Fairness to Enhance Sensitive Attributes Reconstruction
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs +2
In recent years, a growing body of work has emerged on how to learn machine learning models under fairness constraints, often expressed with respect to some sensitive attributes. I…
Learning Fair Rule Lists
Ulrich Aïvodji, Julien Ferry, Sébastien Gambs +2
As the use of black-box models becomes ubiquitous in high stake decision-making systems, demands for fair and interpretable models are increasing. While it has been shown that inte…