Expected Improvement (EI) is arguably the most popular acquisition function in Bayesian
optimization and has found countless successful applications, but its performance is often …

In the past decade, advances in deep learning have resulted in breakthroughs in a variety of
areas, including computer vision, natural language understanding, speech recognition, and …

Many real-world problems can be phrased as a multi-objective optimization problem, where
the goal is to identify the best set of compromises between the competing objectives. Multi …

Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical,
sequential setting of Bayesian Optimization does not translate well into laboratory …

Gaussian process upper confidence bound (GP-UCB) is a theoretically promising approach
for black-box optimization; however, the confidence parameter $\beta $ is considerably large …

Gaussian processes are the model of choice in Bayesian optimization and active learning.
Yet, they are highly dependent on cleverly chosen hyperparameters to reach their full …

Bayesian optimization devolves the global optimization of a costly objective function to the
global optimization of a sequence of acquisition functions. This inner-loop optimization can …

Batch Bayesian optimisation and Bayesian quadrature have been shown to be sample-
efficient methods of performing optimisation and quadrature where expensive-to-evaluate …

Bayesian optimization is a methodology to optimize black-box functions. Traditionally, it
focuses on the setting where you can arbitrarily query the search space. However, many real …

Real-world optimisation problems often feature complex combinations of (1) diverse
constraints,(2) discrete and mixed spaces, and are (3) highly parallelisable.(4) There are …