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Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces
[article]
2020
arXiv
pre-print
Bayesian optimisation is a popular method for efficient optimisation of expensive black-box functions. Traditionally, BO assumes that the search space is known. However, in many problems, this assumption does not hold. To this end, we propose a novel BO algorithm which expands (and shifts) the search space over iterations based on controlling the expansion rate thought a hyperharmonic series. Further, we propose another variant of our algorithm that scales to high dimensions. We show
arXiv:2009.02539v4
fatcat:csoqxbi62vdxxgbefxmmh2uqhu