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Privacy Amplification via Iteration for Shuffled and Online PNSGD
We then analyze the online setting and provide a faster decaying scheme for the magnitude of the injected noise that also guarantees the convergence of privacy loss.
Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic
This paper proposes SplitSGD, a new dynamic learning rate schedule for stochastic optimization.
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