Hanselle, J. M., Hüllermeier, E., Mohr, F., Ngonga Ngomo, A.-C., Sherif, M., Tornede, A., &; Wever, M. D. (2023). Configuration and Evaluation. In C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, &; H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol. 412, pp. 85–104). Heinz Nixdorf Institut, Universität Paderborn.https://doi.org/10.5281/zenodo.8068466 

Schede, E., Brandt, J., Tornede, A., Wever, M. D., Bengs, V., Hüllermeier, E., &; Tierney, K. (2022). A Survey of Methods for Automated Algorithm Configuration. In arXiv:2202.01651.

Tornede, A., Gehring, L., Tornede, T., Wever, M. D., &; Hüllermeier, E. (2022). Algorithm Selection on a Meta Level. In Machine Learning.

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

Wever, M. D., Tornede, A., Mohr, F., &; Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1.https://doi.org/10.1109/tpami.2021.3051276 

Mohr, F., Wever, M. D., Tornede, A., &; Hüllermeier, E. (n.d.). Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.

Tornede, T., Tornede, A., Wever, M. D., &; Hüllermeier, E. (2021). Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference.

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

Mohr, F., &; Wever, M. D. (2021). Replacing the Ex-Def Baseline in AutoML by Naive AutoML. 8th ICML Workshop on Automated Machine Learning, Virtual.

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

Wever, M. D. (2021). Automated Machine Learning for Multi-Label Classification.https://doi.org/10.17619/UNIPB/1-1302 

Hanselle, J. M., Tornede, A., Wever, M. D., &; Hüllermeier, E. (2021). Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021), Delhi, India.

Tornede, A., Wever, M. D., &; Hüllermeier, E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. Discovery Science. Discovery Science 2020.

Hanselle, J. M., Tornede, A., Wever, M. D., &; Hüllermeier, E. (2020). Hybrid Ranking and Regression for Algorithm Selection. KI 2020: Advances in Artificial Intelligence. 43rd German Conference on Artificial Intelligence.

Tornede, T., Tornede, A., Wever, M. D., Mohr, F., &; Hüllermeier, E. (2020). AutoML for Predictive Maintenance: One Tool to RUL Them All. Proceedings of the ECMLPKDD 2020. IOTStream Workshop @ ECMLPKDD 2020.https://doi.org/10.1007/978-3-030-66770-2_8 

Heid, S. H., Wever, M. D., &; Hüllermeier, E. (n.d.). Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. In Journal of Data Mining and Digital Humanities. episciences.

Tornede, A., Wever, M. D., &; Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. Workshop MetaLearn 2020 @ NeurIPS 2020. Workshop MetaLearn 2020 @ NeurIPS 2020, Online.

Tornede, A., Wever, M. D., Werner, S., Mohr, F., &; Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. ACML 2020. 12th Asian Conference on Machine Learning, Bangkok, Thailand.

Wever, M. D., Tornede, A., Mohr, F., &; Hüllermeier, E. (n.d.). LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Symposium on Intelligent Data Analysis, Konstanz, Germany.

Wever, M. D., van Rooijen, L., &; Hamann, H. (2020). Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary Computation, 28(2), 165–193.https://doi.org/10.1162/evco_a_00266