9 citations · 17 across the 3 of their papers we have counts for
6 papers
Probabilistic Dataset Reconstruction from Interpretable Models
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs +2
Interpretability is often pointed out as a key requirement for trustworthy machine learning. However, learning and releasing models that are inherently interpretable leaks informat…
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…
Optimizing Binary Decision Diagrams with MaxSAT for classification
Hao Hu, Marie-José Huguet, Mohamed Siala
The growing interest in explainable artificial intelligence (XAI) for critical decision making motivates the need for interpretable machine learning (ML) models. In fact, due to th…
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…
On the Complexity of Robust Stable Marriage
Begum Genc, Mohamed Siala, Gilles Simonin +1
Robust Stable Marriage (RSM) is a variant of the classical Stable Marriage problem, where the robustness of a given stable matching is measured by the number of modifications requi…
Finding Robust Solutions to Stable Marriage
Begum Genc, Mohamed Siala, Barry O'Sullivan +1
We study the notion of robustness in stable matching problems. We first define robustness by introducing (a,b)-supermatches. An -supermatch is a stable matching in which if…