Bayesian optimization (BO) is a framework for global optimization of expensive-to-evaluate
objective functions. Classical BO methods assume that the objective function is a black box …

Decision making in uncertain scenarios is an ubiquitous challenge in real world systems.
Tools to deal with this challenge include simulations to gather information and statistical …

We consider Bayesian optimization of the output of a network of functions, where each
function takes as input the output of its parent nodes, and where the network takes …

Bayesian optimization is a sample-efficient black-box optimization procedure that is typically
applied to a small number of independent objectives. However, in practice we often wish to …

The acquisition function, a critical component in Bayesian optimization (BO), can often be
written as the expectation of a utility function under a surrogate model. However, to ensure …

We consider the problem of finding an input to a stochastic black box function such that the
scalar output of the black box function is as close as possible to a target value in the sense …

The avoidance of scrap and the adherence to tolerances is an important goal in
manufacturing. This requires a good engineering understanding of the underlying process …

Several fundamental problems in science and engineering consist of global optimization
tasks involving unknown high-dimensional (black-box) functions that map a set of …

Bayesian optimization (BO) has proven to be an effective paradigm for the global
optimization of expensive-to-sample systems. One of the main advantages of BO is its use of …

Emukit is a highly flexible Python toolkit for enriching decision making under uncertainty with
statistical emulation. It is particularly pertinent to complex processes and simulations where …