4 citations · 5 across the 4 of their papers we have counts for
3 papers · 1 filter
Discovering Latent Groups for Robust Classification
Ankur Garg, Ulrich Aïvodji, Samira Ebrahimi Kahou +1
Machine learning models exploit spurious correlations, achieving high average accuracy but failing disproportionately on underrepresented subgroups. Existing methods address this b…
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…
Fool SHAP with Stealthily Biased Sampling
Gabriel Laberge, Ulrich Aïvodji, Satoshi Hara +2
SHAP explanations aim at identifying which features contribute the most to the difference in model prediction at a specific input versus a background distribution. Recent studies h…