Accelerating Non-Smooth and Heavy-Tailed Sampling
Anchored Langevin dynamics (ALD) is useful for non-smooth sampling where the density of the target distribution is possibly non-differentiable and heavy-tailed; reflected anchored Langevin dynamics (RALD) can sample possibly non-differentiable target density on a constrained domain. In this paper, we propose and study non-reversible anchored Langevin dynamics (NALD) for sampling possibly non-differentiable and heavy-tailed target density in the Euclidean space and the non-reversible reflected anchored Langevin dynamics (NRALD) for sampling possibly non-differentiable target density in the constrained space. Our construction adds a circulation drift generated by a possibly state-dependent ...
| Authors | Pervez Ali, Xiaoyu Wang, Yingli Wang et al. |
|---|---|
| Primary category | stat.ML |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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