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arXiv · 2403.09429

VISA: Variational Inference with Sequential Sample-Average Approximations

Heiko Zimmermann·Christian A. Naesseth·Jan-Willem van de Meent
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Abstract

We present variational inference with sequential sample-average approximation (VISA), a method for approximate inference in computationally intensive models, such as those based on numerical simulations. VISA extends importance-weighted forward-KL variational inference by employing a sequence of sample-average approximations, which are considered valid inside a trust region. This makes it possible to reuse model evaluations across multiple gradient steps, thereby reducing computational cost. We perform experiments on high-dimensional Gaussians, Lotka-Volterra dynamics, and a Pickover attractor, which demonstrate that VISA can achieve comparable approximation accuracy to standard importance-weighted forward-KL variational inference with computational savings of a factor two or more for conservatively chosen learning rates.

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Heiko Zimmermann, Christian A. Naesseth, Jan-Willem van de Meent. 2024-03-14. VISA: Variational Inference with Sequential Sample-Average Approximations. https://arxiv.org/abs/2403.09429

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