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Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems
March 6, 2024, 5:42 a.m. | Tom\'as Gonz\'alez, Crist\'obal Guzm\'an, Courtney Paquette
cs.LG updates on arXiv.org arxiv.org
Abstract: We study the problem of differentially-private (DP) stochastic (convex-concave) saddle-points in the polyhedral setting. We propose $(\varepsilon, \delta)$-DP algorithms based on stochastic mirror descent that attain nearly dimension-independent convergence rates for the expected duality gap, a type of guarantee that was known before only for bilinear objectives. For convex-concave and first-order-smooth stochastic objectives, our algorithms attain a rate of $\sqrt{\log(d)/n} + (\log(d)^{3/2}/[n\varepsilon])^{1/3}$, where $d$ is the dimension of the problem and $n$ the dataset size. …
abstract algorithms arxiv convergence cs.cr cs.lg delta gap independent math.oc stochastic study type
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