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Differential Privacy of Gradient Descent on Perturbed Objectives

arXiv:2610.02716v1 Announce Type: cross Abstract: Objective perturbation adds a random linear term to a regularized empirical risk and releases the exact perturbed minimizer. We study the finite computation obtained by releasing the $N$-th iterate of deterministic gradient descent on $w\mapsto F(w;…

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  1. 2026-10-05 04:00 · arXiv stat.ML
    Differential Privacy of Gradient Descent on Perturbed Objectives

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