On Bayesian Quadrature Estimators

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• Masha Naslidnyk (University College London)
• Thursday 26 June 2025, 11:15-12:15
• Seminar Room 1, Newton Institute.

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RCLW03 - Accelerating statistical inference and experimental design with machine learning

Approximating intractable integrals is a common task in both statistical and scientific computation. When evaluating the function under the integral is computationally expensive—-such as when the function is the output of a fine-grained simulation—-standard Monte Carlo integration can become impractical. This creates a need for for methods that approximate integrals well with as few samples as possible. Bayesian quadrature is a probabilistic integration method in which a Gaussian process prior is placed on the integrand, allowing information about properties of the integrand—-such as smoothness—-to be used for improved sample efficiency. In this talk, I will cover two projects that used Bayesian quadrature to create better estimators: (1) an improved estimator for maximum mean discrepancy when the measure is a pushforward, and (2) estimators for conditional and nested expectations.

This talk is part of the Isaac Newton Institute Seminar Series series.

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