Random Forests and related tree-based methods are popular for supervised learning from
table based data. Apart from their ease of parallelization, their classification performance is …

Many industrial and scientific applications require optimization of one or more objectives by
tuning dozens or hundreds of input parameters. While Bayesian optimization has been a …

Bayesian optimization has attracted huge attention from diverse research areas in science
and engineering, since it is capable of finding a global optimum of an expensive-to-evaluate …

Bayesian optimization is a principled optimization strategy for a black-box objective function.
It shows its effectiveness in a wide variety of real-world applications such as scientific …

In this study, we introduce an innovative approach to enhance interpretability in the design
optimization of voxel-based soft robots (VSRs). VSRs present a unique challenge in …

Many real-world scientific and engineering problems can be formulated as instances of goal-
driven adaptive experimental design, wherein candidate experiments are chosen …

Pharmacometrics and the utilization of population pharmacokinetics play an integral role in
model informed drug discovery and development (MIDD). Recently, there has been a growth …

Information can be measured and represented on many different scales and encoded in
multiple formats. The operations that can be used on the data depend on the type of scale …

Abstract Black-box constrained Bayesian Optimisation is a method to optimise black-box
objective functions, that are expensive to evaluate, and subject to black-box constraint …

For temperature-sensitive transportation, it is essential to maintain controlled internal
conditions to ensure products maintain their quality upon arrival. Effective airflow and …