R\'enyi Tracking Bounds for Langevin Dynamics with Moving Targets

Abstract:We study Langevin diffusion and Langevin Monte Carlo (LMC) when the target distribution changes over time. Under a log-Sobolev inequality (LSI), we derive non-asymptotic Rényi-divergence guarantees for tracking the current target. The framework covers continuous-time Langevin diffusion and its discretizations. We then apply the results to nonsmooth sampling based on successive Moreau envelopes. For this scheme, we give explicit choices of the smoothing parameters and step sizes, together with corresponding complexity bounds. To our knowledge, these are the first non-asymptotic Rényi-divergence tracking bounds for Langevin dynamics with discrete target updates.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2609.17577 [stat.ML]

(or arXiv:2609.17577v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2609.17577 Focus to learn more arXiv-issued DOI via DataCite

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