10 citations · 19 across the 5 of their papers we have counts for
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cs.LG2026
Detectability without Interference: Efficient Canary Design for Privacy Auditing in One Run
Mathieu Dagréou, Aurélien Bellet, Valentin Dorseuil +2
Privacy auditing aims to empirically assess privacy leakage in machine learning models using membership inference attacks (MIAs), and to derive lower bounds on differential privacy…
cs.LG2022★ 9 cited
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
Thomas Moreau, Mathurin Massias, Alexandre Gramfort +18
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice.…