English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We consider the problem of sampling from a target distribution, which is\emph {not necessarily logconcave}, in the context of empirical riskminimization and stochastic optimization as presented in Raginsky et al.(2017). Non-asymptotic analysis results are established in the$L^1$-Wasserstein distance for the behaviour of Stochastic Gradient LangevinDynamics (SGLD) algorithms. We allow the estimation of gradients to beperformed even in the presence of \emph{dependent} data streams. Ourconvergence estimates are sharper and \emph{uniform} in the number ofiterations, in contrast to those in previous studies.

On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case