  Contraction in Wasserstein 1-distance with explicit rates is established for
generalized Hamiltonian Monte Carlo with stochastic gradients under possibly
nonconvex conditions. The algorithms considered include splitting schemes of
kinetic Langevin diffusion. As consequence, quantitative Gaussian concentration
bounds are provided for empirical averages. Convergence in Wasserstein
2-distance and total variation are also given, together with numerical bias
estimates.
