Natural Evolution Strategies as a Black Box Estimator for Stochastic
  Variational Inference
Ahmad Ayaz Amin
Stochastic variational inference and its derivatives in the form of
variational autoencoders enjoy the ability to perform Bayesian inference on
large datasets in an efficient manner. However, performing inference with a VAE
requires a certain design choice (i.e. reparameterization trick) to allow
unbiased and low variance gradient estimation, restricting the types of models
that can be created. To overcome this challenge, an alternative estimator based
on natural evolution strategies is proposed. This estimator does not make
assumptions about the kind of distributions used, allowing for the creation of
models that would otherwise not have been possible under the VAE framework.