Automated Machine Learning (AutoML) supports practitioners and researchers with the
tedious task of designing machine learning pipelines and has recently achieved substantial …

The success of machine learning in a broad range of applications has led to an ever-
growing demand for machine learning systems that can be used off the shelf by non-experts …

While early AutoML frameworks focused on optimizing traditional ML pipelines and their
hyperparameters, a recent trend in AutoML is to focus on neural architecture search. In this …

Ensembles over neural network weights trained from different random initialization, known
as deep ensembles, achieve state-of-the-art accuracy and calibration. The recently …

Many years have passed since Baesens et al. published their benchmarking study of
classification algorithms in credit scoring [Baesens, B., Van Gestel, T., Viaene, S …

Comparing different AutoML frameworks is notoriously challenging and often done
incorrectly. We introduce an open and extensible benchmark that follows best practices and …

This survey aims at providing multimedia researchers with a state-of-the-art overview of
fusion strategies, which are used for combining multiple modalities in order to accomplish …

Ensemble selection deals with the reduction of an ensemble of predictive models in order to
improve its efficiency and predictive performance. The last 10 years a large number of very …

Crowdfunding platforms offer founders the possibility to collect funding for project realization.
With the advent of these platforms, the risk of fraud has risen. Fraudulent founders provide …

Algorithm selection and hyperparameter tuning remain two of the most challenging tasks in
machine learning. Automated machine learning (AutoML) seeks to automate these tasks to …