19 citations · 41 across the 7 of their papers we have counts for
10 papers
AutoQML: A Framework for Automated Quantum Machine Learning
Marco Roth, David A. Kreplin, Daniel Basilewitsch +7
Automated Machine Learning (AutoML) has significantly advanced the efficiency of ML-focused software development by automating hyperparameter optimization and pipeline construction…
auto-sktime: Automated Time Series Forecasting
Marc-André Zöller, Marius Lindauer, Marco F. Huber
In today's data-driven landscape, time series forecasting is pivotal in decision-making across various sectors. Yet, the proliferation of more diverse time series data, coupled wit…
Bringing Quantum Algorithms to Automated Machine Learning: A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms
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…
Automated Machine Learning for Remaining Useful Life Predictions
Marc-André Zöller, Fabian Mauthe, Peter Zeiler +2
Being able to predict the remaining useful life (RUL) of an engineering system is an important task in prognostics and health management. Recently, data-driven approaches to RUL pr…
Benchmarking Automated Machine Learning Methods for Price Forecasting Applications
Horst Stühler, Marc-André Zöller, Dennis Klau +2
Price forecasting for used construction equipment is a challenging task due to spatial and temporal price fluctuations. It is thus of high interest to automate the forecasting proc…
Practitioner Motives to Use Different Hyperparameter Optimization Methods
Niclas Kannengießer, Niklas Hasebrook, Felix Morsbach +5
Programmatic hyperparameter optimization (HPO) methods, such as Bayesian optimization and evolutionary algorithms, are highly sample-efficient in identifying optimal hyperparameter…
XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning
Marc-André Zöller, Waldemar Titov, Thomas Schlegel +1
In the last ten years, various automated machine learning (AutoM ) systems have been proposed to build end-to-end machine learning (ML) pipelines with minimal human interaction. Ev…
Incremental Search Space Construction for Machine Learning Pipeline Synthesis
Marc-André Zöller, Tien-Dung Nguyen, Marco F. Huber
Automated machine learning (AutoML) aims for constructing machine learning (ML) pipelines automatically. Many studies have investigated efficient methods for algorithm selection an…
AVATAR -- Machine Learning Pipeline Evaluation Using Surrogate Model
Tien-Dung Nguyen, Tomasz Maszczyk, Katarzyna Musial +2
The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-ba…
Benchmark and Survey of Automated Machine Learning Frameworks
Marc-André Zöller, Marco F. Huber
Machine learning (ML) has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data sc…