Approximation Based Variance Reduction for Reparameterization Gradients

Tomas Geffner; Justin Domke

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arXiv · 2007.14634

Approximation Based Variance Reduction for Reparameterization Gradients

Tomas Geffner·Justin Domke

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Abstract

Flexible variational distributions improve variational inference but are harder to optimize. In this work we present a control variate that is applicable for any reparameterizable distribution with known mean and covariance matrix, e.g. Gaussians with any covariance structure. The control variate is based on a quadratic approximation of the model, and its parameters are set using a double-descent scheme by minimizing the gradient estimator's variance. We empirically show that this control variate leads to large improvements in gradient variance and optimization convergence for inference with non-factorized variational distributions.

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Tomas Geffner, Justin Domke. 2020-07-29. Approximation Based Variance Reduction for Reparameterization Gradients. https://arxiv.org/abs/2007.14634

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