Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference

Part of Advances in Neural Information Processing Systems 30 (NIPS 2017)

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Geoffrey Roeder, Yuhuai Wu, David K. Duvenaud


<p>We propose a simple and general variant of the standard reparameterized gradient estimator for the variational evidence lower bound. Specifically, we remove a part of the total derivative with respect to the variational parameters that corresponds to the score function. Removing this term produces an unbiased gradient estimator whose variance approaches zero as the approximate posterior approaches the exact posterior. We analyze the behavior of this gradient estimator theoretically and empirically, and generalize it to more complex variational distributions such as mixtures and importance-weighted posteriors.</p>