  We present a coupling framework to upper bound the total variation mixing
time of various Metropolis-adjusted, gradient-based Markov kernels in the `high
acceptance regime'. The approach uses a localization argument to boost local
mixing of the underlying unadjusted kernel to mixing of the adjusted kernel
when the acceptance rate is suitably high. As an application, mixing time
guarantees are developed for a non-reversible, adjusted Markov chain based on
the kinetic Langevin diffusion, where little is currently understood.
