Sun, Zhua;
Oates, Chris J;
Briol, François-Xavier;
(2023)
Meta-learning Control Variates: Variance Reduction with Limited Data.
In: Lawrence, Neil, (ed.)
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence.
(pp. pp. 2047-2057).
PMLR: Pittsburgh, PA, USA.
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Abstract
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.
| Type: | Proceedings paper |
|---|---|
| Title: | Meta-learning Control Variates: Variance Reduction with Limited Data |
| Event: | Conference on Uncertainty in Artificial Intelligence (UAI) 2023 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://proceedings.mlr.press/v216/ |
| Language: | English |
| Additional information: | This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10177283 |
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