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cs.LG2026
Beyond Square Roots: Explicit Memory-Efficient Factorization for Multi-Epoch Private Learning
Nikita P. Kalinin, Aki Rehn, Joel Daniel Andersson +2
Correlated-noise mechanisms are among the most promising approaches for improving the utility of differentially private model training, but rigorous guarantees require explicit, an…
cs.LG2026
Learning Rate Scheduling with Matrix Factorization for Private Training
Nikita P. Kalinin, Joel Daniel Andersson
We study differentially private model training with stochastic gradient descent under learning rate scheduling and correlated noise. Although correlated noise, in particular via ma…
cs.LG2025
Streaming Private Continual Counting via Binning
Joel Daniel Andersson, Rasmus Pagh
In differential privacy, refers to problems in which we wish to continuously release a function of a dataset that is revealed one element at a time…