Chen, Zonghao;
Naslidnyk, Masha;
Gretton, Arthur;
Briol, François-Xavier;
(2024)
Conditional Bayesian Quadrature.
In:
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence.
Association for Uncertainty in Artificial Intelligence (AUAI)
Preview |
Text
chen24b.pdf Download (2MB) | Preview |
Abstract
We propose a novel approach for estimating conditional or parametric expectations in the setting where obtaining samples or evaluating integrands is costly. Through the framework of probabilistic numerical methods (such as Bayesian quadrature), our novel approach allows to incorporates prior information about the integrands especially the prior smoothness knowledge about the integrands and the conditional expectation. As a result, our approach provides a way of quantifying uncertainty and leads to a fast convergence rate, which is confirmed both theoretically and empirically on challenging tasks in Bayesian sensitivity analysis, computational finance and decision making under uncertainty.
| Type: | Proceedings paper |
|---|---|
| Title: | Conditional Bayesian Quadrature |
| Event: | UAI 2024, Fortieth Conference on Uncertainty in Artificial Intelligence, 15-19 July 2024, Barcelona, Spain |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://proceedings.mlr.press/v244/chen24b.html |
| Language: | English |
| Additional information: | Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/) |
| 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/10193887 |
Archive Staff Only
 |
View Item |
