NeurIPS 2019poster24 citations
Provable Gradient Variance Guarantees for Black-Box Variational Inference
Abstract
Recent variational inference methods use stochastic gradient estimators whose variance is not well understood. Theoretical guarantees for these estimators are important to understand when these methods will or will not work. This paper gives bounds for the common “reparameterization” estimators when the target is smooth and the variational family is a location-scale distribution. These bounds are unimprovable and thus provide the best possible guarantees under the stated assumptions.
BibTeX
@inproceedings{NEURIPS2019_bd4c9ab7,
author = {Domke, Justin},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Provable Gradient Variance Guarantees for Black-Box Variational Inference},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/bd4c9ab730f5513206b999ec0d90d1fb-Paper.pdf},
volume = {32},
year = {2019}
}