TL;DR本文研究隐私保护随机优化问题在凸性和非凸性情况下的解决方案,并给出了针对不同损失函数的最优算法,其中包括non-smooth generalized linear losses(GLLs)和多种约束条件以及多种平滑系数的情况。
Abstract
We study differentially privatestochastic optimization in convex and non-convex settings. For the convex case, we focus on the family of non-smooth generalized linear losses (→