2 citations · 2 across the 16 of their papers we have counts for
4 papers · 1 filter
kTULA: A Langevin sampling algorithm with improved KL bounds under super-linear log-gradients
Iosif Lytras, Sotirios Sabanis, Ying Zhang
Motivated by applications in deep learning, where the global Lipschitz continuity condition is often not satisfied, we examine the problem of sampling from distributions with super…
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…