The rapid emergence of massive datasets in various fields poses a serious challenge to
traditional statistical methods. Meanwhile, it provides opportunities for researchers to …

Data are distributed across different sites due to computing facility limitations or data privacy
considerations. Conventional centralized methods—those in which all datasets are stored …

We propose a novel, efficient approach for distributed sparse learning with observations
randomly partitioned across machines. In each round of the proposed method, worker …

The growing size of modern data brings many new challenges to existing statistical
inference methodologies and theories, and calls for the development of distributed …

Modal regression is a good alternative of the mean regression and likelihood based
methods, because of its robustness and high efficiency. A robust communication-efficient …

Objectives To characterize the complex interplay between multiple clinical conditions in a
time-to-event analysis framework using data from multiple hospitals, we developed two …

This article studies distributed estimation and inference for a general statistical problem with
a convex loss that could be nondifferentiable. For the purpose of efficient computation, we …

This paper focuses on generalization performance analysis for distributed algorithms in the
framework of learning theory. Taking distributed kernel ridge regression (DKRR) for …

Modern statistical analysis often encounters datasets with large sizes. For these datasets,
conventional estimation methods can hardly be used immediately because practitioners …

Federated learning of causal estimands may greatly improve estimation efficiency by
leveraging data from multiple study sites, but robustness to heterogeneity and model …