We consider stochastic convex optimization for heavy-tailed data with the guarantee of
being differentially private (DP). Most prior works on differentially private stochastic convex …

We study the problem of fitting the high dimensional sparse linear regression model with sub-
Gaussian covariates and responses, where the data are provided by strategic or self …

In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization
(DP-SCO) in Euclidean and general $\ell_p^ d $ spaces. Specifically, we focus on three …

In this paper we study the problem of (finite horizon tabular) Markov decision processes
(MDPs) with heavy-tailed rewards under the constraint of differential privacy (DP) …

In this paper, we study the Differentially Private Empirical Risk Minimization (DP-ERM)
problem, considering both convex and non-convex loss functions. For cases where DP-ERM …

We study the problem of Differentially Private Stochastic Convex Optimization (DPSCO) with
heavy-tailed data. Specifically, we focus on the problem of Least Absolute Deviations, ie, ℓ 1 …

Abstract (Gradient) Expectation Maximization (EM) is a widely used algorithm for estimating
the maximum likelihood of mixture models or incomplete data problems. A major challenge …

Emerging distributed applications recently boosted the development of decentralized
machine learning, especially in IoT and edge computing fields. In real-world scenarios, the …

In this chapter we take up the problem of machine learning for private or sensitive data. The
phrase “privacy-preserving machine learning” can refer to myriad models for privacy and …

As one of the most fundamental non-convex learning problems, ReLU regression under
differential privacy (DP) constraints, especially in high-dimensional settings, remains a …