1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2026
DP-λCGD: Efficient Noise Correlation for Differentially Private Model Training
Nikita P. Kalinin, Ryan McKenna, Rasmus Pagh +1
Differentially private stochastic gradient descent (DP-SGD) is the gold standard for training machine learning models with formal differential privacy guarantees. Several recent ex…
cs.CR2025★ 1 cited
Back to Square Roots: An Optimal Bound on the Matrix Factorization Error for Multi-Epoch Differentially Private SGD
Nikita P. Kalinin, Ryan McKenna, Jalaj Upadhyay +1
Matrix factorization mechanisms for differentially private training have emerged as a promising approach to improve model utility under privacy constraints. In practical settings,…
cs.LG2024
Scaling up the Banded Matrix Factorization Mechanism for Differentially Private ML
Ryan McKenna
Correlated noise mechanisms such as DP Matrix Factorization (DP-MF) have proven to be effective alternatives to DP-SGD in large-epsilon few-epoch training regimes. Significant work…