Can ML models be trained under fairness constraints with formal DP guarantees, without sacrificing utility? New paper at ICLR 2026: RaCO-DP Poster: Pavilion 4 (later today!) 🧵 (w/ @tudorcebere, Michael Menart, Aurélien Bellet, @NicolasPapernot )

Unlike regularization-based methods, RaCO-DP enforces bounds directly by construction: no hyperparameter tuning, no extra privacy cost. Also scales to many simultaneous constraints without degradation, and runs three orders of magnitude faster than the prior SOTA, DP-FERMI.