Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference

This is especially prevalent in machine learning (Schulman et al., 2015), including variational inference (Ranganath et al., 2014; Rezende et al., 2014) and reinforcement learning (Silver et al., 2014). On the face of it, problems of this nature require solving an intractable integral. Most practical approaches instead use Monte Carlo estimates of expectations and their gradients. These techniques are unbiased but can suffer from high variance when sample size is small--one unlikely sample in the tail of a distribution can heavily skew the final estimate. A simple way to reduce variance is to increase the number of samples; however the computational cost grows quickly.