TY  - GEN
A1  - Naslidnyk, Mariia
A1  - Gonzalez, Javier
A1  - Mahsereci, Maren
CY  - Ithaca (NY), USA
EP  - 10
ID  - discovery10165972
AV  - public
TI  - Invariant Priors for Bayesian Quadrature
N1  - This version is the author manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Y1  - 2021/12/02/
UR  - https://doi.org/10.48550/arXiv.2112.01578
N2  - 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.
PB  - Cornell University
ER  -