Reviews: Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction

Update: The authors have helpfully pointed out that they do provide some guidelines on setting the hyperparameters. This paper creatively combines underdamped Langevin MCMC work of Cheng et al. with the gradient estimator SPIDER of Fang et al. This allows the paper to use the theoretical result from Fang et al. to prove a better bound for achieving epsilon in 2-Wasserstein distance. Effectively it is the UL-MCMC algorithm with a better gradient estimator. This isn't meant to imply that the work is trivial as adapting any insight from the optimisation literature for use in a HMC algorithm requires careful work to yield measurable improvements.