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Adaptive Hamiltonian-based MCMC samplers

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In this RCC we discuss the widely-experienced difficulty in tuning Monte Carlo samplers based on simulating Hamiltonian dynamics. We develop an algorithm that allows for the adaptation of Hamiltonian and Riemann manifold Hamiltonian Monte Carlo samplers using Bayesian optimization that allows for infinite adaptation of the parameters of these samplers. We show that the resulting samplers are ergodic, and that the use of our adaptive algorithms makes it easy to obtain more efficient samplers, in some cases precluding the need for more complex solutions. Hamiltonian-based Monte Carlo samplers are widely known to be an excellent choice of MCMC method, and such approaches remove a key obstacle towards the more widespread use of these samplers in practice.

This talk is part of the Machine Learning Reading Group @ CUED series.

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