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

This paper presents the results and insights from the black-box optimization (BBO)
challenge at NeurIPS2020 which ran from July–October, 2020. The challenge emphasized …

Bayesian optimization provides sample-efficient global optimization for a broad range of
applications, including automatic machine learning, engineering, physics, and experimental …

Designing protein sequences towards desired properties is a fundamental goal of protein
engineering, with applications in drug discovery and enzymatic engineering. Machine …

We develop BatchBALD, a tractable approximation to the mutual information between a
batch of points and model parameters, which we use as an acquisition function to select …

Bayesian optimization has recently emerged as a popular method for the sample-efficient
optimization of expensive black-box functions. However, the application to high-dimensional …

A key challenge in leveraging data augmentation for neural network training is choosing an
effective augmentation policy from a large search space of candidate operations. Properly …

Develo** a scalable manufacturing technique for perovskite solar cells requires process
optimization in high-dimensional parameter space. Herein, we present a machine learning …

Neural networks dominate the modern machine learning landscape, but their training and
success still suffer from sensitivity to empirical choices of hyperparameters such as model …