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most citedBack to Square Roots: An Optimal Bound on the Matrix Factorization Error for Multi-Epoch Differentially Private SGD

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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…