Abstract Machine learning algorithms have been used widely in various applications and
areas. To fit a machine learning model into different problems, its hyper-parameters must be …

In the literature, machine learning (ML) and artificial intelligence (AI) applications tend to
start with examples that are irrelevant to process engineers (eg classification of images …

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 …

The dominant paradigm of experiments in the social and behavioral sciences views an
experiment as a test of a theory, where the theory is assumed to generalize beyond the …

Hyperparameter optimization constitutes a large part of typical modern machine learning
(ML) workflows. This arises from the fact that ML methods and corresponding preprocessing …

In this work we rigorously analyse assumptions inherent to black-box optimisation hyper-
parameter tuning tasks. Our results on the Bayesmark benchmark indicate that …

Black-box optimization (BBO) has a broad range of applications, including automatic
machine learning, engineering, physics, and experimental design. However, it remains a …

Decision making in uncertain scenarios is an ubiquitous challenge in real world systems.
Tools to deal with this challenge include simulations to gather information and statistical …

Bayesian optimization (BO) is a successful methodology to optimize black-box functions that
are expensive to evaluate. While traditional methods optimize each black-box function in …