Author

B. Leimkuhler

h-index 4 View on Semantic Scholar

We have 1 of 14 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

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 Monte Carlo.

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