论文标题
私人随机凸优化：非平滑目标的有效算法
Private Stochastic Convex Optimization: Efficient Algorithms for Non-smooth Objectives
论文作者
论文摘要
在本文中，我们重新审视了私人随机凸优化的问题。我们提出了一种基于嘈杂的镜像下降的算法，该算法在统计复杂性和查询数量方面达到了最佳速率，当时隐私参数与样品数量成反比时，在该制度中的一阶随机甲骨文中达到了最佳速率。
In this paper, we revisit the problem of private stochastic convex optimization. We propose an algorithm based on noisy mirror descent, which achieves optimal rates both in terms of statistical complexity and number of queries to a first-order stochastic oracle in the regime when the privacy parameter is inversely proportional to the number of samples.
