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Lookup NU author(s): Professor Chris Oates
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© UAI 2023. All rights reserved.Control variates can be a powerful tool to reduce the variance of Monte Carlo estimators, but constructing effective control variates can be challenging when the number of samples is small. In this paper, we show that when a large number of related integrals need to be computed, it is possible to leverage the similarity between these integration tasks to improve performance even when the number of samples per task is very small. Our approach, called meta learning CVs (Meta-CVs), can be used for up to hundreds or thousands of tasks. Our empirical assessment indicates that Meta-CVs can lead to significant variance reduction in such settings, and our theoretical analysis establishes general conditions under which Meta-CVs can be successfully trained.
Author(s): Sun Z, Oates CJ, Briol F-X
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Uncertainty in Artificial Intelligence
Year of Conference: 2023
Pages: 2047-2057
Online publication date: 08/05/2023
Acceptance date: 02/04/2023
ISSN: 2640-3498
Publisher: ML Research Press
URL: https://proceedings.mlr.press/v216/sun23a.html
Series Title: Proceedings of Machine Learning Research
