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Stochastic optimization techniques are standard in variational inferencealgorithms. These methods estimate gradients by approximating expectations withindependent Monte Carlo samples. In this paper, we explore a technique thatuses correlated, but more representative , samples to reduce estimatorvariance. Specifically, we show how to generate antithetic samples that matchsample moments with the true moments of an underlying importance distribution.Combining a differentiable antithetic sampler with modern stochasticvariational inference, we showcase the effectiveness of this approach forlearning a deep generative model.

Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference