Most machine learning algorithms are configured by a set of hyperparameters whose values
must be carefully chosen and which often considerably impact performance. To avoid a time …

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 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 …

Two-dimensional (2D) material research is rapidly evolving to broaden the spectrum of
emergent 2D systems. Here, we review recent advances in the theory, synthesis …

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 has recently emerged as a popular method for the sample-efficient
optimization of expensive black-box functions. However, the application to high-dimensional …

Bayesian optimisation is a statistical method that efficiently models and optimises expensive
“black-box” functions. This review considers the application of Bayesian optimisation to …

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 …