Chen, Zonghao;
Naslidnyk, Mariia;
Briol, Francois-Xavier;
(2025)
Nested Expectations with Kernel Quadrature.
In:
Proceedings of the 42 nd International Conference on Machine Learning.
PMLR: Vancouver, Canada.
(In press).
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Abstract
This paper considers the challenging computational task of estimating nested expectations. Existing algorithms, such as nested Monte Carlo or multilevel Monte Carlo, are known to be consistent but require a large number of samples at both inner and outer levels to converge. Instead, we propose a novel estimator consisting of nested kernel quadrature estimators and we prove that it has a faster convergence rate than all baseline methods when the integrands have sufficient smoothness. We then demonstrate empirically that our proposed method does indeed require fewer samples to estimate nested expectations on real-world applications including Bayesian optimisation, option pricing, and health economics.
| Type: | Proceedings paper |
|---|---|
| Title: | Nested Expectations with Kernel Quadrature |
| Event: | 42 nd International Conference on Machine Learning |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://icml.cc/ |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10211532 |
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