3 papers
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…
math.PR2023
Taming under isoperimetry
Iosif Lytras, Sotirios Sabanis
In this article we propose a novel taming Langevin-based scheme called to sample from distributions with superlinearly growing log-gradient which also satisfy a Lo…
math.PR2023
Kinetic Langevin MCMC Sampling Without Gradient Lipschitz Continuity -- the Strongly Convex Case
Tim Johnston, Iosif Lytras, Sotirios Sabanis
In this article we consider sampling from log concave distributions in Hamiltonian setting, without assuming that the objective gradient is globally Lipschitz. We propose two algor…