AISTATS 2024poster5 citations
Joint control variate for faster black-box variational inference
Xi Wang, Tomas Geffner, Justin Domke
Abstract
Black-box variational inference performance is sometimes hindered by the use of gradient estimators with high variance. This variance comes from two sources of randomness: Data subsampling and Monte Carlo sampling. While existing control variates only address Monte Carlo noise, and incremental gradient methods typically only address data subsampling, we propose a new "joint" control variate that jointly reduces variance from both sources of noise. This significantly reduces gradient variance, leading to faster optimization in several applications.
BibTeX
@InProceedings{pmlr-v238-wang24c,
title = {Joint control variate for faster black-box variational inference},
author = {Wang, Xi and Geffner, Tomas and Domke, Justin},
booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
pages = {1639--1647},
year = {2024},
editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen},
volume = {238},
series = {Proceedings of Machine Learning Research},
month = {02--04 May},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v238/wang24c/wang24c.pdf},
url = {https://proceedings.mlr.press/v238/wang24c.html},
abstract = {Black-box variational inference performance is sometimes hindered by the use of gradient estimators with high variance. This variance comes from two sources of randomness: Data subsampling and Monte Carlo sampling. While existing control variates only address Monte Carlo noise, and incremental gradient methods typically only address data subsampling, we propose a new "joint" control variate that jointly reduces variance from both sources of noise. This significantly reduces gradient variance, leading to faster optimization in several applications.}
}