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- W4287670475 abstract "We propose an algorithm for Bayesian functional optimisation - that is, finding the function to optimise a process - guided by experimenter beliefs and intuitions regarding the expected characteristics (length-scale, smoothness, cyclicity etc.) of the optimal solution encoded into the covariance function of a Gaussian Process. Our algorithm generates a sequence of finite-dimensional random subspaces of functional space spanned by a set of draws from the experimenter's Gaussian Process. Standard Bayesian optimisation is applied on each subspace, and the best solution found used as a starting point (origin) for the next subspace. Using the concept of effective dimensionality, we analyse the convergence of our algorithm and provide a regret bound to show that our algorithm converges in sub-linear time provided a finite effective dimension exists. We test our algorithm in simulated and real-world experiments, namely blind function matching, finding the optimal precipitation-strengthening function for an aluminium alloy, and learning rate schedule optimisation for deep networks." @default.
- W4287670475 created "2022-07-25" @default.
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- W4287670475 date "2020-09-08" @default.
- W4287670475 modified "2023-09-30" @default.
- W4287670475 title "Sequential Subspace Search for Functional Bayesian Optimization Incorporating Experimenter Intuition" @default.
- W4287670475 doi "https://doi.org/10.48550/arxiv.2009.03543" @default.
- W4287670475 hasPublicationYear "2020" @default.
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