Published on May 20, 2017 by Microsoft Research

Bayesian optimisation (BO) is an optimisation method which incrementally builds a statistical model of the objective function to refine its search. Unfortunately, due to the curse of dimensionality, BO can fail to converge in problems with many dimensions. In this talk, I will show how better priors for BO can result in orders of magnitude improvements in convergence for problems with a known structure. I will introduce a class of models that are both useful and support inference at a reasonable computational cost.

See more on this video at www.microsoft.com/en-us/research/video/bayesian-optimisation-many-dimensions-bespoke-models/

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3 Comments on "Bayesian optimisation in many dimensions with bespoke models"

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Bob Looter
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Bob Looter
2 months 1 day ago

you are boring me out.. fuck off..

Bob Looter
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Bob Looter
2 months 1 day ago

the distribution is same since they do not change…

Bob Looter
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Bob Looter
2 months 1 day ago

all applications owns same dimension's.. it does not matter what inheritance they wanna make a solution for…

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