Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of …

Bayesian optimization provides sample-efficient global optimization for a broad range of
applications, including automatic machine learning, engineering, physics, and experimental …

Recent interest in complex and computationally expensive machine learning models with
many hyperparameters, such as automated machine learning (AutoML) frameworks and …

Neural architecture search (NAS) is a promising research direction that has the potential to
replace expert-designed networks with learned, task-specific architectures. In order to help …

Bayesian Optimisation (BO) refers to a class of methods for global optimisation of a function f
which is only accessible via point evaluations. It is typically used in settings where f is …

Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best suited for optimization over continuous domains of …

Performance of machine learning algorithms depends critically on identifying a good set of
hyperparameters. While recent approaches use Bayesian optimization to adaptively select …

We design and analyse variations of the classical Thompson sampling (TS) procedure for
Bayesian optimisation (BO) in settings where function evaluations are expensive but can be …

This article provides an overview of multi-fidelity modeling trends. Fidelity in modeling refers
to the level of detail and accuracy provided by a predictive model or simulation. Generally …