arXiv preprint, 2025.
In brief
Standard Langevin samplers struggle with non-differentiable potentials (as in L1-regularized models) and explore heavy-tailed targets slowly, because the gradient vanishes far from the mode. Anchoring the sampler to a smooth reference potential, with a multiplicative correction that preserves the target, gives non-asymptotic guarantees without the bias of smoothing — and, with a logarithmic anchor, exponential convergence on heavy-tailed targets where standard Langevin algorithms are only sub-exponential.
Cite
@misc{gurbuzbalaban2025anchored,
title = {Anchored Langevin algorithms},
author = {Mert Gürbüzbalaban and Hoang M. Nguyen and Xicheng Zhang and Lingjiong Zhu},
year = {2025},
howpublished = {arXiv preprint arXiv:2509.19455},
}Relatedsame topics