Bringing Quantum Algorithms to Automated Machine Learning: A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms

no code implementations • 6 Oct 2023 • Dennis Klau, Marc Zöller, Christian Tutschku

This work describes the selection approach and analysis of existing AutoML frameworks regarding their capability of a) incorporating Quantum Machine Learning (QML) algorithms into this automated solving approach of the AutoML framing and b) solving a set of industrial use-cases with different ML problem types by benchmarking their most important characteristics.

AutoML Benchmarking +1

Practitioner Motives to Select Hyperparameter Optimization Methods

no code implementations • 3 Mar 2022 • Niklas Hasebrook, Felix Morsbach, Niclas Kannengießer, Marc Zöller, Jörg Franke, Marius Lindauer, Frank Hutter, Ali Sunyaev

Advanced programmatic hyperparameter optimization (HPO) methods, such as Bayesian optimization, have high sample efficiency in reproducibly finding optimal hyperparameter values of machine learning (ML) models.

Bayesian Optimization BIG-bench Machine Learning +1