Active coevolutionary learning of requirements specifications from examples. - Marcel Wever; Lorijn van Rooijen; Heiko Hamann (GECCO, 2017)

On-the-Fly Service Construction with Prototypes. - Felix Mohr; Marcel Wever; Eyke Hüllermeier (SCC, 2018)

(WIP) Towards the Automated Composition of Machine Learning Services. - Felix Mohr; Marcel Wever; Eyke Hüllermeier; Amin Faez (SCC, 2018)

Ensembles of evolved nested dichotomies for classification. - Marcel Wever; Felix Mohr; Eyke Hüllermeier (GECCO, 2018)

Reduction Stumps for Multi-class Classification. - Felix Mohr; Marcel Wever; Eyke Hüllermeier (IDA, 2018)

Automated Machine Learning Service Composition. - Felix Mohr; Marcel Wever; Eyke Hüllermeier (CoRR, 2018)

Automated Multi-Label Classification based on ML-Plan. - Marcel Wever; Felix Mohr; Eyke Hüllermeier (CoRR, 2018)

ML-Plan: Automated machine learning via hierarchical planning. - Felix Mohr; Marcel Wever; Eyke Hüllermeier (Mach. Learn., 2018)

From Automated to On-The-Fly Machine Learning. - Felix Mohr; Marcel Wever; Alexander Tornede; Eyke Hüllermeier (GI-Jahrestagung, 2019)

Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. - Alexander Tornede; Marcel Wever; Stefan Werner; Felix Mohr; Eyke Hüllermeier (ACML, 2020)

Extreme Algorithm Selection with Dyadic Feature Representation. - Alexander Tornede; Marcel Wever; Eyke Hüllermeier (DS, 2020)

LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-label Classification. - Marcel Wever; Alexander Tornede; Felix Mohr; Eyke Hüllermeier (IDA, 2020)

Hybrid Ranking and Regression for Algorithm Selection. - Jonas Hanselle; Alexander Tornede; Marcel Wever; Eyke Hüllermeier (KI, 2020)

AutoML for Predictive Maintenance: One Tool to RUL Them All. - Tanja Tornede; Alexander Tornede; Marcel Wever; Felix Mohr; Eyke Hüllermeier (IoT Streams/ITEM@PKDD/ECML, 2020)

Towards Meta-Algorithm Selection. - Alexander Tornede; Marcel Wever; Eyke Hüllermeier (CoRR, 2020)

Multioracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. - Marcel Wever; Lorijn van Rooijen; Heiko Hamann (Evol. Comput., 2020)

Coevolution of remaining useful lifetime estimation pipelines for automated predictive maintenance. - Tanja Tornede; Alexander Tornede; Marcel Wever; Eyke Hüllermeier (GECCO, 2021)

Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. - Jonas Hanselle; Alexander Tornede; Marcel Wever; Eyke Hüllermeier (PAKDD (1), 2021)

Naive Automated Machine Learning - A Late Baseline for AutoML. - Felix Mohr; Marcel Wever (CoRR, 2021)

Annotation Uncertainty in the Context of Grammatical Change. - Marie-Luis Merten; Marcel Wever; Michaela Geierhos; Doris Tophinke; Eyke Hüllermeier (CoRR, 2021)

Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. - Eyke Hüllermeier; Felix Mohr; Alexander Tornede; Marcel Wever (CoRR, 2021)

Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. - Felix Mohr; Marcel Wever; Alexander Tornede; Eyke Hüllermeier (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

AutoML for Multi-Label Classification: Overview and Empirical Evaluation. - Marcel Wever; Alexander Tornede; Felix Mohr; Eyke Hüllermeier (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Hyperparameter optimization in deep multi-target prediction. - Dimitrios Iliadis; Marcel Wever; Bernard De Baets; Willem Waegeman (CoRR, 2022)

A Survey of Methods for Automated Algorithm Configuration. - Elias Schede; Jasmin Brandt; Alexander Tornede; Marcel Wever; Viktor Bengs; Eyke Hüllermeier; Kevin Tierney (J. Artif. Intell. Res., 2022)

A flexible class of dependence-aware multi-label loss functions. - Eyke Hüllermeier; Marcel Wever; Eneldo Loza Mencía; Johannes Fürnkranz; Michael Rapp (Mach. Learn., 2022)

Automated machine learning for multi-label classification. - Marcel Wever (2022)

Cooperative Co-Evolution for Ensembles of Nested Dichotomies for Multi-Class Classification. - Marcel Wever; Miran Özdogan; Eyke Hüllermeier (GECCO, 2023)

Meta-learning for Automated Selection of Anomaly Detectors for Semi-supervised Datasets. - David Schubert; Pritha Gupta; Marcel Wever (IDA, 2023)

A Survey of Methods for Automated Algorithm Configuration (Extended Abstract). - Elias Schede; Jasmin Brandt; Alexander Tornede; Marcel Wever; Viktor Bengs; Eyke Hüllermeier; Kevin Tierney (IJCAI, 2023)

Iterative Deepening Hyperband. - Jasmin Brandt; Marcel Wever; Dimitrios Iliadis; Viktor Bengs; Eyke Hüllermeier (CoRR, 2023)

Towards Green Automated Machine Learning: Status Quo and Future Directions. - Tanja Tornede; Alexander Tornede; Jonas Hanselle; Felix Mohr; Marcel Wever; Eyke Hüllermeier (J. Artif. Intell. Res., 2023)

Naive automated machine learning. - Felix Mohr; Marcel Wever (Mach. Learn., 2023)

Algorithm selection on a meta level. - Alexander Tornede; Lukas Gehring; Tanja Tornede; Marcel Wever; Eyke Hüllermeier (Mach. Learn., 2023)

PyExperimenter: Easily distribute experiments and track results. - Tanja Tornede; Alexander Tornede; Lukas Fehring; Lukas Gehring; Helena Graf; Jonas Hanselle; Felix Mohr; Marcel Wever (J. Open Source Softw., 2023)

Position: Why We Must Rethink Empirical Research in Machine Learning. - Moritz Herrmann; F. Julian D. Lange; Katharina Eggensperger; Giuseppe Casalicchio; Marcel Wever; Matthias Feurer; David Rügamer; Eyke Hüllermeier; Anne-Laure Boulesteix; Bernd Bischl (ICML, 2024)

Best Arm Identification with Retroactively Increased Sampling Budget for More Resource-Efficient HPO. - Jasmin Brandt; Marcel Wever; Viktor Bengs; Eyke Hüllermeier (IJCAI, 2024)

MetaQuRe: Meta-learning from Model Quality and Resource Consumption. - Raphael Fischer 001; Marcel Wever; Sebastian Buschjäger; Thomas Liebig (ECML/PKDD (7), 2024)

Hyperparameter optimization of two-branch neural networks in multi-target prediction. - Dimitrios Iliadis; Marcel Wever; Bernard De Baets; Willem Waegeman (Appl. Soft Comput., 2024)

Automated Machine Learning for Multi-Label Classification. - Marcel Wever (CoRR, 2024)

ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data. - Valentin Margraf; Marcel Wever; Sandra Gilhuber; Gabriel Marques Tavares; Thomas Seidl 001; Eyke Hüllermeier (CoRR, 2024)

Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. - Stefan Heid; Marcel Wever; Eyke Hüllermeier (J. Data Min. Digit. Humanit., 2024)

SynthACticBench: A Capability-Based Synthetic Benchmark for Algorithm Configuration. - Valentin Margraf; Anna Lappe; Marcel Wever; Carolin Benjamins; Eyke Hüllermeier; Marius Lindauer (GECCO, 2025)

Information leakage detection through approximate Bayes-optimal prediction. - Pritha Gupta; Marcel Wever; Eyke Hüllermeier (Inf. Sci., 2025)

OpenML: Insights from 10 years and more than a thousand papers. - Bernd Bischl; Giuseppe Casalicchio; Taniya Das; Matthias Feurer; Sebastian Fischer; Pieter Gijsbers; Subhaditya Mukherjee; Andreas C. Müller 002; László Németh; Luis Oala; Lennart Purucker; Sahithya Ravi; et al. (Patterns, 2025)

HyperSHAP: Shapley Values and Interactions for Hyperparameter Importance. - Marcel Wever; Maximilian Muschalik; Fabian Fumagalli; Marius Lindauer (CoRR, 2025)