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 (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 …

Automated Machine Learning (AutoML) supports practitioners and researchers with the
tedious task of designing machine learning pipelines and has recently achieved substantial …

Optimizing multiple competing black-box objectives is a challenging problem in many fields,
including science, engineering, and machine learning. Multi-objective Bayesian optimization …

Bayesian optimization (BO) is increasingly employed in critical applications such as
materials design and drug discovery. An increasingly popular strategy in BO is to forgo the …

To achieve peak predictive performance, hyperparameter optimization (HPO) is a crucial
component of machine learning and its applications. Over the last years, the number of …

Tackling the most pressing problems for humanity, such as the climate crisis and the threat
of global pandemics, requires accelerating the pace of scientific discovery. While science …

Nanoporous materials (NPMs) could be used to store, capture, and sense many different
gases. Given an adsorption task, we often wish to search a library of NPMs for the one with …

Black-box optimization (BBO) has a broad range of applications, including automatic
machine learning, engineering, physics, and experimental design. However, it remains a …

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 …