Author

P. Whalley

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Preprint Oct 2026

Theoretical guarantees for stochastic gradient Langevin dynamics

We prove asymptotic bias bounds for stochastic gradient Langevin dynamics in Wasserstein distance of order two. We assume that the negative log-density is strongly convex with a Lipschitz gradient, and that the stochastic gradient estimator is unbiased with an error satisfying a mean-square Lipschitz condition. The bou...

Daniel Paulin, P. Whalley · 0 citations

Open access Aug 2026

Unbiased kinetic Langevin Monte Carlo with inexact gradients

Theoretical analysis demonstrates that the proposed estimator is unbiased, attains finite variance, and satisfies a central limit theorem, and the results demonstrate that in large-scale applications, the unbiased algorithm can be 2–3 orders of magnitude more efficient than the “gold-standard” randomized Hamiltonian Mo...

N. K. Chada, B. Leimkuhler, Daniel Paulin et al. · Annals of Statistics · 0 citations