Automated machine learning (AutoML) supports the algorithmic construction and data-
specific customization of machine learning pipelines, including the selection, combination …

Automated Machine Learning (AutoML) supports practitioners and researchers with the
tedious task of designing machine learning pipelines and has recently achieved substantial …

Algorithm configuration (AC) is concerned with the automated search of the most suitable
parameter configuration of a parametrized algorithm. There is currently a wide variety of AC …

Automated Machine Learning, which supports practitioners and researchers with the tedious
task of manually designing machine learning pipelines, has recently achieved substantial …

Algorithm selection, a critical process of automated machine learning, aims to identify the
most suitable algorithm for solving a specific problem prior to execution. Mainstream …

The problem of selecting an algorithm that appears most suitable for a specific instance of
an algorithmic problem class, such as the Boolean satisfiability problem, is called instance …

Selecting a well-performing algorithm for a given task or dataset can be time-consuming and
tedious, but is crucial for the successful day-to-day business of developing new AI & ML …

Algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of
candidate algorithms most suitable for a specific instance of an algorithmic problem class …

In the algorithm selection research, the discussion surrounding algorithm features has been
significantly overshadowed by the emphasis on problem features. Although a few empirical …

Impressive performance improvements have been achieved in many areas of AI by meta-
algorithmic techniques, such as automated algorithm selection and configuration. However …