  Hamiltonian Monte Carlo (HMC) is a widely used sampler for continuous
probability distributions. In many cases, the underlying Hamiltonian dynamics
exhibit a phenomenon of resonance which decreases the efficiency of the
algorithm and makes it very sensitive to hyperparameter values. This issue can
be tackled efficiently, either via the use of trajectory length randomization
(RHMC) or via partial momentum refreshment. The second approach is connected to
the kinetic Langevin diffusion, and has been mostly investigated through the
use of Generalized HMC (GHMC). However, GHMC induces momentum flips upon
rejections causing the sampler to backtrack and waste computational resources.
In this work we focus on a recent algorithm bypassing this issue, named
Metropolis Adjusted Langevin Trajectories (MALT). We build upon recent
strategies for tuning the hyperparameters of RHMC which target a bound on the
Effective Sample Size (ESS) and adapt it to MALT, thereby enabling the first
user-friendly deployment of this algorithm. We construct a method to optimize a
sharper bound on the ESS and reduce the estimator variance. Easily compatible
with parallel implementation, the resultant Adaptive MALT algorithm is
competitive in terms of ESS rate and hits useful tradeoffs in memory usage when
compared to GHMC, RHMC and NUTS.
