  Effective implementations of sampling-based probabilistic inference often
require manually constructed, model-specific proposals. Inspired by recent
progresses in meta-learning for training learning agents that can generalize to
unseen environments, we propose a meta-learning approach to building effective
and generalizable MCMC proposals. We parametrize the proposal as a neural
network to provide fast approximations to block Gibbs conditionals. The learned
neural proposals generalize to occurrences of common structural motifs across
different models, allowing for the construction of a library of learned
inference primitives that can accelerate inference on unseen models with no
model-specific training required. We explore several applications including
open-universe Gaussian mixture models, in which our learned proposals
outperform a hand-tuned sampler, and a real-world named entity recognition
task, in which our sampler yields higher final F1 scores than classical
single-site Gibbs sampling.
