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Modulated Bayesian Optimisation

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Probabilistic nummerics aims at providing a principled framework of uncertainty to nummerical methods. Importantly this allows different methods to be contrasted as their implicit assumptions have to be made explicit. In this talk I will introduce the idea of probabilistic nummerics and provide a formulation of Bayesian optimisation within this framework. In specific I will present a set of surrogage models that focuses on the details that are informative for search while ignoring detrimental structure that are challenging to model from few observations. We demonstrate that surrogate models with appropriate noise distributions can absorb challenging structures in the objective function by treating them as irreducible uncertainty.

This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.

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