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 …

This paper provides a tutorial overview of recent advances in learning control policy
representations for complex systems. We focus on control policies that are determined by …

Data annotation is a time-consuming and labor-intensive process for many NLP tasks.
Although there exist various methods to produce pseudo data labels, they are often task …

Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …

Bayesian Optimisation (BO) refers to a suite of techniques for global optimisation of
expensive black box functions, which use introspective Bayesian models of the function to …

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 …

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 …

High throughput experimentation in heterogeneous catalysis provides an efficient solution to
the generation of large datasets under reproducible conditions. Knowledge extraction from …

The ever-increasing capability of computational methods has resulted in their general
acceptance as a key part of the materials design process. Traditionally this has been …

Physics-informed machine learning (PIML) is a set of methods and tools that systematically
integrate machine learning (ML) algorithms with physical constraints and abstract …