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cs.LG2026
Causal Evaluation of Membership Inference Attacks
Mathieu Even, Clément Berenfeld, Linus Bleistein +3
Membership Inference Attacks (MIAs) aim to distinguish training points (members) from unseen data (non-members), and are widely used to quantify memorization and assess privacy ris…
cs.LG2026
Set-Valued Policy Learning
Laura Fuentes-Vicente, Mathieu Even, Gaëlle Dormion +3
Conventional treatment policies map patient covariates to a single recommended intervention in order to maximize expected clinical outcomes. Although a rich body of causal inferenc…
cs.LG2026
Model Agnostic Differentially Private Causal Inference
Christian Janos Lebeda, Mathieu Even, Aurélien Bellet +1
Estimating causal effects from observational data is essential in fields such as medicine, economics and social sciences, where privacy concerns are paramount. We propose a general…