35 citations · 64 across the 19 of their papers we have counts for
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stat.ML2026
On the Inherent Privacy Amplification of Missing Data
Simon Roburin, Rafaël Pinot, Rafa{ë}l Pinot +1
Privacy preservation is critical in many high-stakes domains such as medicine and finance, where sensitive data must be analyzed without compromising individual confidentiality. At…
stat.ML2025
Fairness Meets Privacy: Integrating Differential Privacy and Demographic Parity in Multi-class Classification
Lilian Say, Christophe Denis, Rafael Pinot
The increasing use of machine learning in sensitive applications demands algorithms that simultaneously preserve data privacy and ensure fairness across potentially sensitive sub-p…