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

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 …

Hyperparameters play a crucial role in the model selection of machine learning algorithms.
Tuning these hyperparameters can be exhaustive when the data is large. Bayesian …

Experimental optimization is prevalent in many areas of artificial intelligence including
machine learning. Conventional methods like grid search and random search can be …

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 …

With the extensive applications of machine learning models, automatic hyperparameter
optimization (HPO) has become increasingly important. Motivated by the tuning behaviors of …

The tuning of hyperparameters becomes increasingly important as machine learning (ML)
models have been extensively applied in data mining applications. Among various …

Bayesian optimisation is a well-known sample-efficient method for the optimisation of
expensive black-box functions. However when dealing with big search spaces the algorithm …

Meta-learning is a powerful approach that exploits historical data to quickly solve new tasks
from the same distribution. In the low-data regime, methods based on the closed-form …

This paper studies the regret bound of two transfer learning algorithms in Bayesian
optimisation. The first algorithm models any difference between the source and target …