Timezone: »
Bayesian optimization has demonstrated impressive success in finding the optimum input x∗ and output f∗ = f(x∗) = max f(x) of a black-box function f. In some applications, however, the optimum output is known in advance and the goal is to find the corresponding optimum input. Existing work in Bayesian optimization (BO) has not effectively exploited the knowledge of f∗ for optimization. In this paper, we consider a new setting in BO in which the knowledge of the optimum output is available. Our goal is to exploit the knowledge about f∗ to search for the input x∗ efficiently. To achieve this goal, we first transform the Gaussian process surrogate using the information about the optimum output. Then, we propose two acquisition functions, called confidence bound minimization and expected regret minimization, which exploit the knowledge about the optimum output to identify the optimum input more efficient. We show that our approaches work intuitively and quantitatively better performance against standard BO methods. We demonstrate real applications in tuning a deep reinforcement learning algorithm on the CartPole problem and XGBoost on Skin Segmentation dataset in which the optimum values are publicly available.
Author Information
Vu Nguyen (University of Oxford)
Michael A Osborne (U Oxford)
More from the Same Authors
-
2021 : Attacking Graph Classification via Bayesian Optimisation »
Xingchen Wan · Henry Kenlay · Binxin Ru · Arno Blaas · Michael A Osborne · Xiaowen Dong -
2021 : Revisiting Design Choices in Offline Model Based Reinforcement Learning »
Cong Lu · Philip Ball · Jack Parker-Holder · Michael A Osborne · Stephen Roberts -
2022 Poster: Robust Multi-Objective Bayesian Optimization Under Input Noise »
Samuel Daulton · Sait Cakmak · Maximilian Balandat · Michael A Osborne · Enlu Zhou · Eytan Bakshy -
2022 Spotlight: Robust Multi-Objective Bayesian Optimization Under Input Noise »
Samuel Daulton · Sait Cakmak · Maximilian Balandat · Michael A Osborne · Enlu Zhou · Eytan Bakshy -
2021 Workshop: Challenges in Deploying and monitoring Machine Learning Systems »
Alessandra Tosi · Nathan Korda · Michael A Osborne · Stephen Roberts · Andrei Paleyes · Fariba Yousefi -
2021 Poster: Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces »
Xingchen Wan · Vu Nguyen · Huong Ha · Binxin Ru · Cong Lu · Michael A Osborne -
2021 Poster: Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search »
Vu Nguyen · Tam Le · Makoto Yamada · Michael A Osborne -
2021 Spotlight: Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces »
Xingchen Wan · Vu Nguyen · Huong Ha · Binxin Ru · Cong Lu · Michael A Osborne -
2021 Spotlight: Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search »
Vu Nguyen · Tam Le · Makoto Yamada · Michael A Osborne -
2020 : 1.9 Bayesian optimization for Iterative Learning »
Vu Nguyen -
2020 : Contributed Talk 1: Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits »
Jack Parker-Holder · Vu Nguyen · Stephen Roberts -
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: Fast Information-theoretic Bayesian Optimisation »
Binxin Ru · Michael A Osborne · Mark Mcleod · Diego Granziol -
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 -
2018 Oral: Fast Information-theoretic Bayesian Optimisation »
Binxin Ru · Michael A Osborne · Mark Mcleod · Diego Granziol