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 …

A wide spectrum of design and decision problems, including parameter tuning, A/B testing
and drug design, intrinsically are instances of black-box optimization. Bayesian optimization …

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

Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

Bayesian optimization (BO) with Gaussian processes (GPs) surrogate models is widely used
to optimize analytically unknown and expensive-to-evaluate functions. In this paper, we …

Bayesian optimization (BO) is a prominent “black-box” optimization approach. It makes
sequential decisions using a Bayesian model, usually a Gaussian process, to effectively …

In this paper we develop a Bayesian optimization based hyperparameter tuning framework
inspired by statistical learning theory for classifiers. We utilize two key facts from PAC …

Due to the increased number of parameters and data in the pre-trained model exceeding a
certain level, a foundation model (eg, a large language model) can significantly improve …

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 …