Generalizing Hamiltonian Monte Carlo with Neural Networks

Abstract

We present a general-purpose method to train Markov chain Monte Carlo kernels, parameterized by deep neural networks, that converge and mix quickly to their target distribution. Our method generalizes Hamiltonian Monte Carlo and is trained to maximize expected squared jumped distance, a proxy for mixing speed. We demonstrate large empirical gains on a collection of simple but challenging distributions, for instance achieving a 106x improvement in effective sample size in one case, and mixing when standard HMC makes no measurable progress in a second. Finally, we show quantitative and qualitative gains on a real-world task: latent-variable generative modeling. We release an open source TensorFlow implementation of the algorithm.

ICLR 2018

References in corpus (3)

• Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
• Accelerate Monte Carlo Simulations with Restricted Boltzmann Machines
• Hamiltonian Annealed Importance Sampling for partition function estimation

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• MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference
• Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling
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