no code implementations • 18 Mar 2024 • Timothée Ly, Julien Ferry, Marie-José Huguet, Sébastien Gambs, Ulrich Aivodji
Differentially-private (DP) mechanisms can be embedded into the design of a machine learningalgorithm to protect the resulting model against privacy leakage, although this often comes with asignificant loss of accuracy.
1 code implementation • 29 Feb 2024 • Julien Ferry, Ricardo Fukasawa, Timothée Pascal, Thibaut Vidal
Even with bootstrap aggregation, the majority of the data can also be reconstructed.
no code implementations • 22 Dec 2023 • Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala
Machine learning techniques are increasingly used for high-stakes decision-making, such as college admissions, loan attribution or recidivism prediction.
no code implementations • 29 Aug 2023 • Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala
In addition, we demonstrate that under realistic assumptions regarding the interpretable models' structure, the uncertainty of the reconstruction can be computed efficiently.
no code implementations • 11 Apr 2023 • Julien Rouzot, Julien Ferry, Marie-José Huguet
In this paper, we use Mixed-Integer Linear Programming (MILP) techniques to produce inherently interpretable scoring systems under sparsity and fairness constraints, for the general multi-class classification setup.
1 code implementation • 8 Mar 2023 • Julien Ferry, Gabriel Laberge, Ulrich Aïvodji
The advantages of such models over classical ones are two-fold: 1) They grant users precise control over the level of transparency of the system and 2) They can potentially perform better than a standalone black box since redirecting some of the inputs to an interpretable model implicitly acts as regularization.
no code implementations • 2 Sep 2022 • Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala
More precisely, we propose a generic reconstruction correction method, which takes as input an initial guess made by the adversary and corrects it to comply with some user-defined constraints (such as the fairness information) while minimizing the changes in the adversary's guess.
1 code implementation • 9 Sep 2019 • Ulrich Aïvodji, Julien Ferry, Sébastien Gambs, Marie-José Huguet, Mohamed Siala
While it has been shown that interpretable models can be as accurate as black-box models in several critical domains, existing fair classification techniques that are interpretable by design often display poor accuracy/fairness tradeoffs in comparison with their non-interpretable counterparts.