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Poster
Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces
Hung Tran-The · Sunil Gupta · Santu Rana · Huong Ha · Svetha Venkatesh

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #1061
in Poster Session 4 »

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.

[ Paper ]

Author Information

Hung Tran-The (Deakin University)
Sunil Gupta (Deakin University)
Santu Rana (Deakin University)
Huong Ha (RMIT University)
Svetha Venkatesh (Deakin University)

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