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Beyond Square Roots: A Memory-Efficient Explicit Factorization for Multi-Epoch Private Learning
Correlated-noise mechanisms are among the most promising approaches for improving the utility of differentially private model training, but rigorous guarantees require explicit, analyzable factorizations, and practical deployment requires memory efficiency. Recent works have developed banded inverse
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