Tornede, A. (2023). Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions.https://doi.org/10.17619/UNIPB/1-1780  

Dellnitz, M., Hüllermeier, E., Lücke, M., Ober-Blöbaum, S., Offen, C., Peitz, S., &; Pfannschmidt, K. (2023). Efficient time stepping for numerical integration using reinforcement  learning. SIAM Journal on Scientific Computing, 45(2), 9–5.https://doi.org/10.1137/21M1412682 

Gevers, K., Schöppner, V., &; Hüllermeier, E. (2021). Heated tool butt welding of two different materials –  Established methods versus artificial intelligence. International Institute of Welding, online.

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.

Bengs, V., Busa-Fekete, R., El Mesaoudi-Paul, A., &; Hüllermeier, E. (2021). Preference-based Online Learning with Dueling Bandits: A Survey. Journal of Machine Learning Research, 22(7), 1–108.

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.

Bernijazov, R., Dicks, A., Dumitrescu, R., Foullois, M., Hanselle, J. M., Hüllermeier, E., Karakaya, G., Ködding, P., Lohweg, V., Malatyali, M., Meyer auf der Heide, F., Panzner, M., &; Soltenborn, C. (2021). A Meta-Review on Artiû01cial Intelligence in Product Creation. Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21). 30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop “AI and Product Design,” Montreal, Kanada.

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.

Damke, C., &; Hüllermeier, E. (2021). Ranking Structured Objects with Graph Neural Networks. In C. Soares &; L. Torgo (Eds.), Proceedings of The 24th International Conference on Discovery Science (DS 2021) (Vol. 12986, pp. 166–180). Springer.https://doi.org/10.1007/978-3-030-88942-5 

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.

Pfannschmidt, K., &; Hüllermeier, E. (2020). Learning Choice Functions via Pareto-Embeddings. In Lecture Notes in Computer Science. Cham.https://doi.org/10.1007/978-3-030-58285-2_30 

Damke, C., Melnikov, V., &; Hüllermeier, E. (2020). A Novel Higher-order Weisfeiler-Lehman Graph Convolution. In S. Jialin Pan &; M. Sugiyama (Eds.), Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020) (Vol. 129, pp. 49–64). Bangkok, Thailand: PMLR.

Bengs, V., &; Hüllermeier, E. (2020). Preselection Bandits. In International Conference on Machine Learning (pp. 778–787).

Bengs, V., &; Hüllermeier, E. (2020). Multi-Armed Bandits with Censored Consumption of Resources. ArXiv:2011.00813.

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