Showing stat.COShow all
3 papers · 1 filter
stat.CO2026
Theoretical guarantees for stochastic gradient sampling methods via Gaussian convolution inequalities
Daniel Paulin, Peter A. Whalley
We derive first-order (in the stepsize) bounds on the bias in Wasserstein distances of the invariant measure of stochastic gradient kinetic Langevin dynamics with minimal assumptio…
stat.CO2025
Divide, Interact, Sample: The Two-System Paradigm
James Chok, Myung Won Lee, Daniel Paulin +1
Mean-field, ensemble-chain, and adaptive samplers have historically been viewed as distinct approaches to Monte Carlo sampling. In this paper, we present a unifying {two-system} fr…
stat.CO2023
Unbiased Kinetic Langevin Monte Carlo with Inexact Gradients
Neil K. Chada, Benedict Leimkuhler, Daniel Paulin +1
We present an unbiased method for Bayesian posterior means based on kinetic Langevin dynamics that combines advanced splitting methods with enhanced gradient approximations. Our ap…