Most machine learning algorithms are configured by a set of hyperparameters whose values
must be carefully chosen and which often considerably impact performance. To avoid a time …

This paper surveys the recent attempts, both from the machine learning and operations
research communities, at leveraging machine learning to solve combinatorial optimization …

Machine learning techniques have deeply rooted in our everyday life. However, since it is
knowledge-and labor-intensive to pursue good learning performance, humans are heavily …

Abstract Machine learning (ML) has become a vital part in many aspects of our daily life.
However, building well performing machine learning applications requires highly …

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 …

Image recognition is one of the primary applications of machine learning algorithms.
Nevertheless, machine learning models used in modern image recognition systems consist …

Due to low numbers of qubits and their error-proneness, Noisy Intermediate-Scale Quantum
(NISQ) computers impose constraints on the size of quantum algorithms they can …

Hyperparameter tuning is an omnipresent problem in machine learning as it is an integral
aspect of obtaining the state-of-the-art performance for any model. Most often …

Hyperparameter tuning is critical for the performance of machine learning algorithms.
However, a noticeable limitation is the high computational cost of algorithm evaluation for …

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