Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces

Part of Advances in Neural Information Processing Systems 33 (NeurIPS 2020)

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Hung Tran-The, Sunil Gupta, Santu Rana, Huong Ha, Svetha Venkatesh


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 \emph{hyperharmonic series}. Further, we propose another variant of our algorithm that scales to high dimensions. We show theoretically that for both our algorithms, the cumulative regret grows at sub-linear rates. Our experiments with synthetic and real-world optimisation tasks demonstrate the superiority of our algorithms over the current state-of-the-art methods for Bayesian optimisation in unknown search space.