The effectiveness of the machine learning methods for real-world tasks depends on the
proper structure of the modeling pipeline. The proposed approach is aimed to automate the …

This work describes the selection approach and analysis of existing AutoML frameworks
regarding their capability of a) incorporating Quantum Machine Learning (QML) algorithms …

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 process …

XAutoML: A Visual Analytics Tool for Establishing Trust in Automated Machine Learning Page 1
XAutoML: A Visual Analytics Tool for Establishing Trust in Automated Machine Learning …

In the last 10 years, various automated machine learning (AutoML) systems have been
proposed to build end-to-end machine learning (ML) pipelines with minimal human …

Forecasting prices of used construction equipment is challenging due to spatial and
temporal price fluctuations. Automating this forecasting process using current market data is …

Automated machine learning (AutoML) systems propose an end-to-end solution to a given
machine learning problem, creating either fixed or flexible pipelines. Fixed pipelines are task …

Feature selection is crucial for enhancing the accuracy and efficiency of machine learning
(ML) models. This work investigates the utility of quantum annealing for the feature selection …

Support vector machines are powerful and frequently used machine learning methods for
classification and regression tasks, which rely on the construction of kernel matrices. While …

We present CLS-Luigi, a framework for synthesizing analytics pipelines that enable
prediction and decision-making. Analytics pipelines typically consist of a number of diverse …