Analysis of Langevin midpoint methods using an anticipative Girsanov theorem

We introduce a new method for analyzing midpoint discretizations of stochastic differential equations (SDEs), which are frequently used in Markov chain Monte Carlo (MCMC) methods for sampling from a target measure π propto exp(-V). Borrowing techniques from Malliavin calculus, we compute estimates for the Radon-Nikodym derivative for processes on L2([0, T); ℝd) which may anticipate the Brownian motion, in the sense that they may not be adapted to the filtration at the same time. Applying these to various popular midpoint discretizations, we are able to improve the regularity and cross-regularity results in the literature on sampling methods. We also obtain a query complexity bound of widetildeO(frackappa5/4 d1/4varepsilon1/2) for obtaining a varepsilon2-accurate sample in mathsfKL divergence, under log-concavity and strong smoothness assumptions for nabla2 V.