Bayesian Optimisation for Constrained Problems

05/27/2021
by   Juan Ungredda, et al.
0

Many real-world optimisation problems such as hyperparameter tuning in machine learning or simulation-based optimisation can be formulated as expensive-to-evaluate black-box functions. A popular approach to tackle such problems is Bayesian optimisation (BO), which builds a response surface model based on the data collected so far, and uses the mean and uncertainty predicted by the model to decide what information to collect next. In this paper, we propose a novel variant of the well-known Knowledge Gradient acquisition function that allows it to handle constraints. We empirically compare the new algorithm with four other state-of-the-art constrained Bayesian optimisation algorithms and demonstrate its superior performance. We also prove theoretical convergence in the infinite budget limit.

READ FULL TEXT
02/05/2020

ε-shotgun: ε-greedy Batch Bayesian Optimisation

Bayesian optimisation is a popular, surrogate model-based approach for o...
09/19/2018

Bayesian functional optimisation with shape prior

Real world experiments are expensive, and thus it is important to reach ...
02/15/2018

Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian Optimisation

The paper presents a novel approach to direct covariance function learni...
12/07/2020

HEBO: Heteroscedastic Evolutionary Bayesian Optimisation

We introduce HEBO: Heteroscedastic Evolutionary Bayesian Optimisation th...
10/04/2018

Infill Criterion for Multimodal Model-Based Optimisation

Physical systems are modelled and investigated within simulation softwar...
11/11/2020

Identifying Properties of Real-World Optimisation Problems through a Questionnaire

Optimisation algorithms are commonly compared on benchmarks to get insig...
12/05/2019

Ordinal Bayesian Optimisation

Bayesian optimisation is a powerful tool to solve expensive black-box pr...