Master Thesis: Optimizer Ensembles for Automated Machine Learning

Thesis about an AutoML approach with two core ideas:

• Model selection as an HTN planning problem searched via an MCTS, which allows the creation of more complex pipelines
• Model configuration via an ensemble of different optimization approaches to detect and afterwards exploit the most suitable optimizer for the given input dataset

This approach has a reference implementation for direct usage.