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

Understand

We consider the problem of sampling from a target distribution, which is \emph {not necessarily logconcave}, in the context of empirical risk minimization 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 Langevin Dynamics (SGLD) algorithms.
• We allow the estimation of gradients to be performed even in the presence of \emph{dependent} data streams.