Private Stochastic Convex Optimization: Efficient Algorithms for Non-smooth Objectives
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
We study the differentially private Empirical Risk Minimization (ERM) and Stochastic Convex Optimization (SCO) problems for non-smooth convex functions. We get a (nearly) optimal bound on the excess empirical risk and ex…
Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings
We study differentially private stochastic optimization in convex and non-convex settings. For the convex case, we focus on the family of non-smooth generalized linear losses (GLLs). Our algorithm for the $\ell_2$ settin…
Stochastic OptimizationBring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization
We study differentially private (DP) algorithms for smooth stochastic minimax optimization, with stochastic minimization as a byproduct. The holy grail of these settings is to guarantee the optimal trade-off between the …
Private Stochastic Convex Optimization: Optimal Rates in Linear Time
We study differentially private (DP) algorithms for stochastic convex optimization: the problem of minimizing the population loss given i.i.d. samples from a distribution over convex loss functions. A recent work of Bass…
Differentially Private Online-to-Batch for Smooth Losses
We develop a new reduction that converts any online convex optimization algorithm suffering $O(\sqrt{T})$ regret into an $\epsilon$-differentially private stochastic convex optimization algorithm with the optimal converg…