ε-shotgun: ε-greedy Batch Bayesian Optimisation

02/05/2020
by   George De Ath, et al.
0

Bayesian optimisation is a popular, surrogate model-based approach for optimising expensive black-box functions. Given a surrogate model, the next location to expensively evaluate is chosen via maximisation of a cheap-to-query acquisition function. We present an ϵ-greedy procedure for Bayesian optimisation in batch settings in which the black-box function can be evaluated multiple times in parallel. Our ϵ-shotgun algorithm leverages the model's prediction, uncertainty, and the approximated rate of change of the landscape to determine the spread of batch solutions to be distributed around a putative location. The initial target location is selected either in an exploitative fashion on the mean prediction, or – with probability ϵ– from elsewhere in the design space. This results in locations that are more densely sampled in regions where the function is changing rapidly and in locations predicted to be good (i.e close to predicted optima), with more scattered samples in regions where the function is flatter and/or of poorer quality. We empirically evaluate the ϵ-shotgun methods on a range of synthetic functions and two real-world problems, finding that they perform at least as well as state-of-the-art batch methods and in many cases exceed their performance.

READ FULL TEXT

page 6

page 14

page 15

research
09/10/2022

Batch Bayesian Optimization via Particle Gradient Flows

Bayesian Optimisation (BO) methods seek to find global optima of objecti...
research
10/15/2020

Asynchronous ε-Greedy Bayesian Optimisation

Bayesian Optimisation (BO) is a popular surrogate model-based approach f...
research
05/27/2021

Bayesian Optimisation for Constrained Problems

Many real-world optimisation problems such as hyperparameter tuning in m...
research
06/09/2022

Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination

Calculation of Bayesian posteriors and model evidences typically require...
research
04/17/2020

What do you Mean? The Role of the Mean Function in Bayesian Optimisation

Bayesian optimisation is a popular approach for optimising expensive bla...
research
12/07/2020

HEBO: Heteroscedastic Evolutionary Bayesian Optimisation

We introduce HEBO: Heteroscedastic Evolutionary Bayesian Optimisation th...
research
09/22/2022

Batch Bayesian optimisation via density-ratio estimation with guarantees

Bayesian optimisation (BO) algorithms have shown remarkable success in a...

Please sign up or login with your details

Forgot password? Click here to reset