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News/Differential Privacy of Gradient Descent on Perturbed Objectives
arxiv
PublishedOctober 5, 2026 at 4:00 AM

Differential Privacy of Gradient Descent on Perturbed Objectives

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arXiv:2610.02716v1 Announce Type: new 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;S)+

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