Automatic structured variational inference

Luca Ambrogioni · Kate Lin · Emily Fertig · Sharad Vikram · Max Hinne · Dave Moore · Marcel van Gerven

Keywords: [ Applications ] [ Privacy, Anonymity, and Security ] [ Probabilistic Methods ] [ Algorithms -> Regression; Algorithms -> Uncertainty Estimation; Probabilistic Methods; Probabilistic Methods ] [ Graphical Model ] [ Probabilistic Programming ]

[ Abstract ]
Thu 15 Apr 7:30 a.m. PDT — 9:30 a.m. PDT


Stochastic variational inference offers an attractive option as a default method for differentiable probabilistic programming. However, the performance of the variational approach depends on the choice of an appropriate variational family. Here, we introduce automatic structured variational inference (ASVI), a fully automated method for constructing structured variational families, inspired by the closed-form update in conjugate Bayesian models. These pseudo-conjugate families incorporate the forward pass of the input probabilistic program and can therefore capture complex statistical dependencies. Pseudo-conjugate families have the same space and time complexity of the input probabilistic program and are therefore tractable for a very large family of models including both continuous and discrete variables. We validate our automatic variational method on a wide range of both low- and high-dimensional inference problems. We find that ASVI provides a clear improvement in performance when compared with other popular approaches such as mean field family and inverse autoregressive flows. We provide a fully automatic open source implementation of ASVI in TensorFlow Probability.

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