An asymptotic analysis of distributed nonparametric methods
Botond Szabó, Harry van Zanten; 20(87):1−30, 2019.
Abstract
We investigate and compare the fundamental performance of several distributed learning methods that have been proposed recently. We do this in the context of a distributed version of the classical signal-in-Gaussian-white-noise model, which serves as a benchmark model for studying performance in this setting. The results show how the design and tuning of a distributed method can have great impact on convergence rates and validity of uncertainty quantification. Moreover, we highlight the difficulty of designing nonparametric distributed procedures that automatically adapt to smoothness.
[abs]
[pdf][bib]| © JMLR 2019. (edit, beta) |