25 citations · 80 across the 7 of their papers we have counts for
4 papers · 1 filter
Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization
Ying Zhang, Ömer Deniz Akyildiz, Theodoros Damoulas +1
In this paper, we are concerned with a non-asymptotic analysis of sampling algorithms used in nonconvex optimization. In particular, we obtain non-asymptotic estimates in Wasserste…
On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case
Ngoc Huy Chau, Éric Moulines, Miklos Rásonyi +2
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 optimizati…
On stochastic gradient Langevin dynamics with dependent data streams in the logconcave case
M. Barkhagen, N. H. Chau, É. Moulines +3
We study the problem of sampling from a probability distribution on $\rset^d$ which has a density \wrt\ the Lebesgue measure known up to a normalization factor $x \mapsto \rme^…
Higher Order Langevin Monte Carlo Algorithm
Sotirios Sabanis, Ying Zhang
A new (unadjusted) Langevin Monte Carlo (LMC) algorithm with improved rates in total variation and in Wasserstein distance is presented. All these are obtained in the context of sa…