3 papers
cs.LG2026
The Tamed Subgradient Unadjusted Langevin Algorithm beyond Convexity
Iosif Lytras, Nikolaos Makras, Sotirios Sabanis
We study the problem of sampling from target distributions whose potentials are simultaneously non-smooth, subject to superlinear gradient growth, and non-convex. We introduce the…
stat.ML2025
The Performance Of The Unadjusted Langevin Algorithm Without Smoothness Assumptions
Tim Johnston, Iosif Lytras, Nikolaos Makras +1
In this article, we study the problem of sampling from distributions whose densities are not necessarily smooth nor logconcave. We propose a simple Langevin-based algorithm that do…
math.PR2025
Taming the Interacting Particle Langevin Algorithm: The Superlinear case
Tim Johnston, Nikolaos Makras, Sotirios Sabanis
Recent advances in stochastic optimization have yielded the interacting particle Langevin algorithm (IPLA), which leverages the notion of interacting particle systems (IPS) to effi…