Nested Expectations with Kernel Quadrature

Zonghao Chen; Masha Naslidnyk; François-Xavier Briol

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arXiv · 2502.18284

Nested Expectations with Kernel Quadrature

Zonghao Chen·Masha Naslidnyk·François-Xavier Briol

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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.

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BibTeX RIS

Zonghao Chen, Masha Naslidnyk, François-Xavier Briol. 2025-02-25. Nested Expectations with Kernel Quadrature. https://arxiv.org/abs/2502.18284

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