Naslidnyk, Mariia;
Gonzalez, Javier;
Mahsereci, Maren;
(2021)
Invariant Priors for Bayesian Quadrature.
Cornell University: Ithaca (NY), USA.
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Abstract
Bayesian quadrature (BQ) is a model-based numerical integration method that is able to increase sample efficiency by encoding and leveraging known structure of the integration task at hand. In this paper, we explore priors that encode invariance of the integrand under a set of bijective transformations in the input domain, in particular some unitary transformations, such as rotations, axis-flips, or point symmetries. We show initial results on superior performance in comparison to standard Bayesian quadrature on several synthetic and one real world application.
| Type: | Working / discussion paper |
|---|---|
| Title: | Invariant Priors for Bayesian Quadrature |
| Event: | NeurIPS 2021 Workshop. Your Model Is Wrong: Robustness and Misspecification in Probabilistic Modeling 2021 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://doi.org/10.48550/arXiv.2112.01578 |
| Language: | English |
| Additional information: | This version is the author 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 UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10165972 |
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