  Informative Bayesian priors are often difficult to elicit, and when this is
the case, modelers usually turn to noninformative or objective priors. However,
objective priors such as the Jeffreys and reference priors are not tractable to
derive for many models of interest. We address this issue by proposing
techniques for learning reference prior approximations: we select a parametric
family and optimize a black-box lower bound on the reference prior objective to
find the member of the family that serves as a good approximation. We
experimentally demonstrate the method's effectiveness by recovering Jeffreys
priors and learning the Variational Autoencoder's reference prior.
