Waegeman, W., Dembczynski, K., &; Hüllermeier, E. (2019). Multi-target prediction: a unifying view on problems and methods. Data Mining and Knowledge Discovery, 33(2), 293–324.https://doi.org/10.1007/s10618-018-0595-5 

Mortier, T., Wydmuch, M., Dembczynski, K., Hüllermeier, E., &; Waegeman, W. (2019). Set-Valued Prediction in Multi-Class Classification. In Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019.

Ahmadi Fahandar, M., &; Hüllermeier, E. (2019). Feature Selection for Analogy-Based Learning to Rank. In Discovery Science. Cham.https://doi.org/10.1007/978-3-030-33778-0_22 

Ahmadi Fahandar, M., &; Hüllermeier, E. (2019). Analogy-Based Preference Learning with Kernels. In KI 2019: Advances in Artificial Intelligence. Cham.https://doi.org/10.1007/978-3-030-30179-8_3 

Nguyen, V.-L., Destercke, S., &; Hüllermeier, E. (2019). Epistemic Uncertainty Sampling. In Discovery Science. Cham.https://doi.org/10.1007/978-3-030-33778-0_7 

Melnikov, V., &; Hüllermeier, E. (2019). Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA. In Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101).https://doi.org/10.1016/j.jmva.2019.02.017 

Epple, N., Dari, S., Drees, L., Protschky, V., &; Riener, A. (2019). Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries. In 2019 IEEE Intelligent Vehicles Symposium (IV).https://doi.org/10.1109/ivs.2019.8814100 

Tornede, A., Wever, M. D., &; Hüllermeier, E. (2019). Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In F. Hoffmann, E. Hüllermeier, &; R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019 (pp. 135–146). Dortmund: KIT Scientific Publishing, Karlsruhe.

Brinker, K., &; Hüllermeier, E. (2019). A Reduction of Label Ranking to Multiclass Classification. In Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany.

Hüllermeier, E., Couso, I., &; Diestercke, S. (2019). Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants. In Proceedings SUM 2019, International Conference on Scalable Uncertainty Management.