Timezone: »
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)
Related Events (a corresponding poster, oral, or spotlight)
-
2018 Oral: Fast Information-theoretic Bayesian Optimisation »
Thu Jul 12th 11:30 -- 11:50 AM Room A3
More from the Same Authors
-
2021 Workshop: Challenges in Deploying and monitoring Machine Learning Systems »
Alessandra Tosi · Nathan Korda · Michael A Osborne · Stephen Roberts · Andrei Paleyes · Fariba Yousefi -
2020 Poster: Knowing The What But Not The Where in Bayesian Optimization »
Vu Nguyen · Michael A Osborne -
2020 Poster: Bayesian Optimisation over Multiple Continuous and Categorical Inputs »
Binxin Ru · Ahsan Alvi · Vu Nguyen · Michael A Osborne · Stephen Roberts -
2019 Poster: On the Limitations of Representing Functions on Sets »
Edward Wagstaff · Fabian Fuchs · Martin Engelcke · Ingmar Posner · Michael A Osborne -
2019 Oral: On the Limitations of Representing Functions on Sets »
Edward Wagstaff · Fabian Fuchs · Martin Engelcke · Ingmar Posner · Michael A Osborne -
2019 Poster: Automated Model Selection with Bayesian Quadrature »
Henry Chai · Jean-Francois Ton · Michael A Osborne · Roman Garnett -
2019 Poster: AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs »
Gabriele Abbati · Philippe Wenk · Michael A Osborne · Andreas Krause · Bernhard Schölkopf · Stefan Bauer -
2019 Poster: Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation »
Ahsan Alvi · Binxin Ru · Jan-Peter Calliess · Stephen Roberts · Michael A Osborne -
2019 Oral: Automated Model Selection with Bayesian Quadrature »
Henry Chai · Jean-Francois Ton · Michael A Osborne · Roman Garnett -
2019 Oral: AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs »
Gabriele Abbati · Philippe Wenk · Michael A Osborne · Andreas Krause · Bernhard Schölkopf · Stefan Bauer -
2019 Oral: Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation »
Ahsan Alvi · Binxin Ru · Jan-Peter Calliess · Stephen Roberts · Michael A Osborne -
2019 Poster: Fingerprint Policy Optimisation for Robust Reinforcement Learning »
Supratik Paul · Michael A Osborne · Shimon Whiteson -
2019 Oral: Fingerprint Policy Optimisation for Robust Reinforcement Learning »
Supratik Paul · Michael A Osborne · Shimon Whiteson -
2018 Poster: Optimization, fast and slow: optimally switching between local and Bayesian optimization »
Mark McLeod · Stephen Roberts · Michael A Osborne -
2018 Oral: Optimization, fast and slow: optimally switching between local and Bayesian optimization »
Mark McLeod · Stephen Roberts · Michael A Osborne