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SUMMARY:On Bayesian Quadrature Estimators - Masha Naslidnyk (University Co
 llege London)
DTSTART:20250626T101500Z
DTEND:20250626T111500Z
UID:TALK232300@talks.cam.ac.uk
DESCRIPTION:Approximating intractable integrals is a common task in both s
 tatistical and scientific computation. When evaluating the function under 
 the integral is computationally expensive---such as when the function is t
 he output of a fine-grained simulation---standard Monte Carlo integration 
 can become impractical. This creates a need for for methods that approxima
 te integrals well with as few samples as possible. Bayesian quadrature is 
 a probabilistic integration method in which a Gaussian process prior is pl
 aced on the integrand\, allowing information about properties of the integ
 rand---such as smoothness---to be used for improved sample efficiency. In 
 this talk\, I will cover two projects that used Bayesian quadrature to cre
 ate better estimators: (1) an improved estimator for maximum mean discrepa
 ncy when the measure is a pushforward\, and (2) estimators for conditional
  and nested expectations.
LOCATION:Seminar Room 1\, Newton Institute
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