Configuration and Evaluation
J.M. Hanselle, E. Hüllermeier, F. Mohr, A.-C. Ngonga Ngomo, M. Sherif, A. Tornede, M.D. Wever, 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, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104.

Configuration and Evaluation
J.M. Hanselle, E. Hüllermeier, F. Mohr, A.-C. Ngonga Ngomo, M. Sherif, A. Tornede, M.D. Wever, 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, Heinz Nixdorf Institut, Universität Paderborn, Paderborn, 2023, pp. 85–104.

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

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

A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials
K. Gevers, A. Tornede, M.D. Wever, V. Schöppner, E. Hüllermeier, Welding in the World (2022).

AutoML for Multi-Label Classification: Overview and Empirical Evaluation
M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) 1–1.

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

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

Automated Machine Learning, Bounded Rationality, and Rational Metareasoning
E. Hüllermeier, F. Mohr, A. Tornede, M.D. Wever, in: 2021.

Replacing the Ex-Def Baseline in AutoML by Naive AutoML
F. Mohr, M.D. Wever, in: 2021.

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

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

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

Extreme Algorithm Selection with Dyadic Feature Representation
A. Tornede, M.D. Wever, E. Hüllermeier, in: Discovery Science, 2020.

Hybrid Ranking and Regression for Algorithm Selection
J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, in: KI 2020: Advances in Artificial Intelligence, 2020.

AutoML for Predictive Maintenance: One Tool to RUL Them All
T. Tornede, A. Tornede, M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the ECMLPKDD 2020, 2020.

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

Towards Meta-Algorithm Selection
A. Tornede, M.D. Wever, E. Hüllermeier, in: Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.

Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis
A. Tornede, M.D. Wever, S. Werner, F. Mohr, E. Hüllermeier, in: ACML 2020, 2020.

LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification
M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, in: Springer, n.d.