  Stochastic optimization techniques are standard in variational inference
algorithms. These methods estimate gradients by approximating expectations with
independent Monte Carlo samples. In this paper, we explore a technique that
uses correlated, but more representative , samples to reduce estimator
variance. Specifically, we show how to generate antithetic samples that match
sample moments with the true moments of an underlying importance distribution.
Combining a differentiable antithetic sampler with modern stochastic
variational inference, we showcase the effectiveness of this approach for
learning a deep generative model.
