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Asynchronous ε-Greedy Bayesian Optimisation
Bayesian Optimisation (BO) is a popular surrogate model-based approach f...
10/15/2020 ∙ by George De Ath, et al. ∙
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Are we Forgetting about Compositional Optimisers in Bayesian Optimisation?
Bayesian optimisation presents a sample-efficient methodology for global...
12/15/2020 ∙ by Antoine Grosnit, et al. ∙
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ε-shotgun: ε-greedy Batch Bayesian Optimisation
Bayesian optimisation is a popular, surrogate model-based approach for o...
02/05/2020 ∙ by George De Ath, et al. ∙
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Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation
Bayesian optimisation is an important decision-making tool for high-stak...
10/17/2019 ∙ by Ryan-Rhys Griffiths, et al. ∙
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Automatic Generation of Algorithms for Black-Box Robust Optimisation Problems
We develop algorithms capable of tackling robust black-box optimisation ...
04/15/2020 ∙ by Martin Hughes, et al. ∙
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Evolutionary Optimisation of Real-Time Systems and Networks
The design space of networked embedded systems is very large, posing cha...
05/06/2019 ∙ by Leandro Soares Indrusiak, et al. ∙
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Ecosystem-Oriented Distributed Evolutionary Computing
We create a novel optimisation technique inspired by natural ecosystems,...
11/23/2012 ∙ by Gerard Briscoe, et al. ∙
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HEBO: Heteroscedastic Evolutionary Bayesian Optimisation
12/07/2020 ∙ by Alexander I. Cowen-Rivers, et al. ∙
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We introduce HEBO: Heteroscedastic Evolutionary Bayesian Optimisation that won the NeurIPS 2020 black-box optimisation competition. We present non-conventional modifications to the surrogate model and acquisition maximisation process and show such a combination superior against all baselines provided by the Bayesmark package. Lastly, we perform an ablation study to highlight the components that contributed to the success of HEBO.
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