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Information-theoretic Bayesian optimisation techniques have demonstrated state-of-the-art performance in tackling important global optimisation problems. However, current information-theoretic approaches require many approximations in implementation, introduce often-prohibitive computational overhead and limit the choice of kernels available to model the objective. We develop a fast information-theoretic Bayesian Optimisation method, FITBO, that avoids the need for sampling the global minimiser, thus significantly reducing computational overhead. Moreover, in comparison with existing approaches, our method faces fewer constraints on kernel choice and enjoys the merits of dealing with the output space. We demonstrate empirically that FITBO inherits the performance associated with information-theoretic Bayesian optimisation, while being even faster than simpler Bayesian optimisation approaches, such as Expected Improvement.
Author Information
Robin Ru (University of Oxford)
Michael A Osborne (U Oxford)
Mark Mcleod (University of Oxford)
Diego Granziol (Oxford)
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2018 Oral: Fast Information-theoretic Bayesian Optimisation »
Thu Jul 12th 11:30 -- 11:50 AM Room A3
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