Si, S;
Oates, CJ;
Duncan, AB;
Carin, L;
Briol, F-X;
(2020)
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization.
ArXiv: Ithaca, NY, USA.
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Abstract
Control variates are a well-established tool to reduce the variance of Monte Carlo estimators. However, for large-scale problems including high-dimensional and large-sample settings, their advantages can be outweighed by a substantial computational cost. This paper considers control variates based on Stein operators, presenting a framework that encompasses and generalizes existing approaches that use polynomials, kernels and neural networks. A learning strategy based on minimising a variational objective through stochastic optimization is proposed, leading to scalable and effective control variates. Novel theoretical results are presented to provide insight into the variance reduction that can be achieved, and an empirical assessment, including applications to Bayesian inference, is provided in support.
| Type: | Working / discussion paper |
|---|---|
| Title: | Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://arxiv.org/pdf/2006.07487v2.pdf |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices 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.ucl.ac.uk/id/eprint/10105444 |
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