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

The discovery of processes for the synthesis of new materials involves many decisions
about process design, operation, and material properties. Experimentation is crucial but as …

Bayesian optimization is a powerful paradigm to optimize black-box functions based on
scarce and noisy data. Its data efficiency can be further improved by transfer learning from …

Online field experiments are the gold-standard way of evaluating changes to real-world
interactive machine learning systems. Yet our ability to explore complex, multi-dimensional …

Bayesian optimization has become a standard technique for hyperparameter optimization of
machine learning algorithms. We consider the setting where previous optimization runs are …

Human-AI collaboration looks at harnessing the complementary strengths of both humans
and AI. We propose a new method for human-AI collaboration in Bayesian optimisation …

The looming end of Moore's Law and ascending use of deep learning drives the design of
custom accelerators that are optimized for specific neural architectures. Architecture …

Transfer in reinforcement learning is usually achieved through generalisation across tasks.
Whilst many studies have investigated transferring knowledge when the reward function …

Bayesian optimization (BO) has become popular for sequential optimization of black-box
functions. When BO is used to optimize a target function, we often have access to previous …

Bayesian optimization usually assumes that a Bayesian prior is given. However, the strong
theoretical guarantees in Bayesian optimization are often regrettably compromised in …