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Invariant Priors for Bayesian Quadrature
Masha Naslidnyk · Javier González · Maren Mahsereci

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in Your Model is Wrong: Robustness and misspecification in probabilistic modeling »

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.

Author Information

Masha Naslidnyk (UCL)
Javier González (Microsoft Research Cambridge)
Maren Mahsereci (University of Tübingen)

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