Contraction Rate Estimates of Stochastic Gradient Kinetic Langevin Integrators. - Benedict J. Leimkuhler; Daniel Paulin; Peter A. Whalley (CoRR, 2023)

Unbiased Kinetic Langevin Monte Carlo with Inexact Gradients. - Neil K. Chada; Benedict J. Leimkuhler; Daniel Paulin; Peter A. Whalley (CoRR, 2023)

Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations". - Daniel Paulin; Peter A. Whalley (CoRR, 2024)

Convergence of kinetic Langevin samplers for non-convex potentials. - Katharina Schuh; Peter A. Whalley (CoRR, 2024)

Contraction and Convergence Rates for Discretized Kinetic Langevin Dynamics. - Benedict J. Leimkuhler; Daniel Paulin; Peter A. Whalley (SIAM J. Numer. Anal., 2024)

Randomized time Riemannian Manifold Hamiltonian Monte Carlo. - Peter A. Whalley; Daniel Paulin; Benedict J. Leimkuhler (Stat. Comput., 2024)

Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics. - Daniel Paulin; Peter A. Whalley; Neil K. Chada; Benedict J. Leimkuhler (AISTATS, 2025)

Random Reshuffling for Stochastic Gradient Langevin Dynamics. - Luke Shaw; Peter A. Whalley (CoRR, 2025)

Randomised Splitting Methods and Stochastic Gradient Descent. - Luke Shaw; Peter A. Whalley (CoRR, 2025)

A Langevin sampling algorithm inspired by the Adam optimizer. - Benedict J. Leimkuhler; René Lohmann; Peter A. Whalley (CoRR, 2025)