J. M. Hanselle et al., “Configuration and Evaluation,” in On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104.

E. Schede et al., “A Survey of Methods for Automated Algorithm Configuration,” arXiv:2202.01651. 2022.

A. Tornede, L. Gehring, T. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection on a Meta Level,” Machine Learning. 2022.

K. Gevers, A. Tornede, M. D. Wever, V. Schöppner, and E. Hüllermeier, “A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials,”10.1007/s40194-022-01339-9__Welding in the World, 2022, doi:10.1007/s40194-022-01339-9 .

M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label Classification: Overview and Empirical Evaluation,”10.1109/tpami.2021.3051276__IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–1, 2021, doi:10.1109/tpami.2021.3051276 .

F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence.

T. Tornede, A. Tornede, M. D. Wever, and E. Hüllermeier, “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance,” presented at the Genetic and Evolutionary Computation Conference, 2021.

E. Hüllermeier, F. Mohr, A. Tornede, and M. D. Wever, “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” presented at the ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual), 2021.

F. Mohr and M. D. Wever, “Replacing the Ex-Def Baseline in AutoML by Naive AutoML,” presented at the 8th ICML Workshop on Automated Machine Learning, Virtual, 2021.

T. Tornede, A. Tornede, J. M. Hanselle, M. D. Wever, F. Mohr, and E. Hüllermeier, “Towards Green Automated Machine Learning: Status Quo and Future Directions,” arXiv:2111.05850. 2021.

M. D. Wever, Automated Machine Learning for Multi-Label Classification. 2021.

J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data.” 2021.

A. Tornede, M. D. Wever, and E. Hüllermeier, “Extreme Algorithm Selection with Dyadic Feature Representation,” presented at the Discovery Science 2020, 2020.

J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Hybrid Ranking and Regression for Algorithm Selection,” presented at the 43rd German Conference on Artificial Intelligence, 2020.

T. Tornede, A. Tornede, M. D. Wever, F. Mohr, and E. Hüllermeier, “AutoML for Predictive Maintenance: One Tool to RUL Them All,”10.1007/978-3-030-66770-2_8__presented at the IOTStream Workshop @ ECMLPKDD 2020, 2020, doi:10.1007/978-3-030-66770-2_8 .

S. H. Heid, M. D. Wever, and E. Hüllermeier, “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction,” Journal of Data Mining and Digital Humanities. episciences.

A. Tornede, M. D. Wever, and E. Hüllermeier, “Towards Meta-Algorithm Selection,” presented at the Workshop MetaLearn 2020 @ NeurIPS 2020, Online, 2020.

A. Tornede, M. D. Wever, S. Werner, F. Mohr, and E. Hüllermeier, “Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis,” presented at the 12th Asian Conference on Machine Learning, Bangkok, Thailand, 2020.

M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification,” presented at the Symposium on Intelligent Data Analysis, Konstanz, Germany.

M. D. Wever, L. van Rooijen, and H. Hamann, “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets,”10.1162/evco_a_00266__Evolutionary Computation, vol. 28, no. 2, pp. 165–193, 2020, doi:10.1162/evco_a_00266 .