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 (AutoML) strives for the automatic configuration of machine
learning algorithms and their composition into an overall (software) solution—a machine …

Automated machine learning (AutoML) seeks to automatically find so-called machine
learning pipelines that maximize the prediction performance when being used to train a …

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 …

We present the first nontrivial procedure for configuring heuristic algorithms to maximize the
utility provided to their end users while also offering theoretical guarantees about …

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 …

The use of machine learning techniques to improve the performance of branch-and-bound
optimization algorithms is a very active area in the context of mixed integer linear problems …

When trying to solve a computational problem, we are often faced with a choice between
algorithms that are guaranteed to return the right answer but differ in their runtime …

It is well known that different algorithms perform differently well on an instance of an
algorithmic problem, motivating algorithm selection (AS): Given an instance of an algorithmic …

In online algorithm selection (OAS), instances of an algorithmic problem class are presented
to an agent one after another, and the agent has to quickly select a presumably best …