21 citations · 79 across the 19 of their papers we have counts for
4 papers · 1 filter
Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations
Di Wang, Adam Smith, Jinhui Xu
Minimizing a convex risk function is the main step in many basic learning algorithms. We study protocols for convex optimization which provably leak very little about the individua…
Differentially Private Empirical Risk Minimization Revisited: Faster and More General
Di Wang, Minwei Ye, Jinhui Xu
In this paper we study the differentially private Empirical Risk Minimization (ERM) problem in different settings. For smooth (strongly) convex loss function with or without (non)-…
Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning
Di Wang, Jinhui Xu
In this paper, we revisit the large-scale constrained linear regression problem and propose faster methods based on some recent developments in sketching and optimization. Our algo…
Empirical Risk Minimization in Non-interactive Local Differential Privacy: Efficiency and High Dimensional Case
Di Wang, Marco Gaboardi, Jinhui Xu
In this paper, we study the Empirical Risk Minimization problem in the non-interactive local model of differential privacy. In the case of constant or low dimensionality ()…