3 papers
math.PR2025
Contractive kinetic Langevin samplers beyond global Lipschitz continuity
Iosif Lytras, Panayotis Mertikopoulos
In this paper, we examine the problem of sampling from log-concave distributions with (possibly) superlinear gradient growth under kinetic (underdamped) Langevin algorithms. Using…
math.ST2025
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…
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…