Variational Boosting: Iteratively Refining Posterior Approximations
Abstract
We propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class. Our method, variational boosting, iteratively refines an existing variational approximation by solving a sequence of optimization problems, allowing a trade-off between computation time and accuracy. We expand the variational approximating class by incorporating additional covariance structure and by introducing new components to form a mixture. We apply variational boosting to synthetic and real statistical models, and show that the resulting posterior inferences compare favorably to existing variational algorithms.
Cite
Text
Miller et al. "Variational Boosting: Iteratively Refining Posterior Approximations." International Conference on Machine Learning, 2017.Markdown
[Miller et al. "Variational Boosting: Iteratively Refining Posterior Approximations." International Conference on Machine Learning, 2017.](https://mlanthology.org/icml/2017/miller2017icml-variational/)BibTeX
@inproceedings{miller2017icml-variational,
title = {{Variational Boosting: Iteratively Refining Posterior Approximations}},
author = {Miller, Andrew C. and Foti, Nicholas J. and Adams, Ryan P.},
booktitle = {International Conference on Machine Learning},
year = {2017},
pages = {2420-2429},
volume = {70},
url = {https://mlanthology.org/icml/2017/miller2017icml-variational/}
}