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) with Gaussian processes (GP) has become an indispensable
algorithm for black box optimization problems. Not without a dash of irony, BO is often …

We demonstrate that a wide array of machine learning algorithms are specific instances of
one single paradigm: reciprocal learning. These instances range from active learning over …

A wide range of machine learning algorithms iteratively add data to the training sample.
Examples include semi-supervised learning, active learning, multi-armed bandits, and …

A wide range of machine learning algorithms iteratively add data to the training sample.
Examples include semi-supervised learning, active learning, multi-armed bandits, and …

Modern parametric and nonparametric estimation methods based on machine learning are
becoming increasingly popular in surveys. This paper intends presenting a synthetic review …