VISA: Variational Inference with Sequential Sample-Average Approximations
Heiko Zimmermann, Christian A. Naesseth, Jan-Willem van de Meent
Abstract
We present variational inference with sequential sample-average approximations (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.
BibTeX
@inproceedings{
zimmermann2024visa,
title={{VISA}: Variational Inference with Sequential Sample-Average Approximations},
author={Heiko Zimmermann and Christian A. Naesseth and Jan-Willem van de Meent},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lbLC5OV9GY}
}