Bengs V, Holzmann H. Uniform approximation in classical weak convergence theory. arXiv:190309864. 2019.

Wever MD, Mohr F, Hüllermeier E, Hetzer A. Towards Automated Machine Learning for Multi-Label Classification. In: ; 2019.

Tagne VK, Fotso S, Fono LA, Hüllermeier E. Choice Functions Generated by Mallows and Plackett–Luce Relations. New Mathematics and Natural Computation. 2019;15(2):191-213.

Couso I, Borgelt C, Hüllermeier E, Kruse R. Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Computational Intelligence Magazine. 2019:31-44. doi:10.1109/mci.2018.2881642

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

Mortier T, Wydmuch M, Dembczynski K, Hüllermeier E, Waegeman W. 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. ; 2019.

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

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

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

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

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

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

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

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

Henzgen S, Hüllermeier E. Mining Rank Data. ACM Transactions on Knowledge Discovery from Data. 2019:1-36. doi:10.1145/3363572

Bengs V, Eulert M, Holzmann H. Asymptotic confidence sets for the jump curve in bivariate regression problems. Journal of Multivariate Analysis. 2019:291-312. doi:10.1016/j.jmva.2019.02.017

Bengs V, Holzmann H. Adaptive confidence sets for kink estimation. Electronic Journal of Statistics. 2019:1523-1579. doi:10.1214/19-ejs1555

Mohr F, Wever MD, Tornede A, Hüllermeier E. From Automated to On-The-Fly Machine Learning. In: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik. Bonn: Gesellschaft für Informatik e.V.; 2019:273-274.

Wever MD, Mohr F, Tornede A, Hüllermeier E. Automating Multi-Label Classification Extending ML-Plan. In: ; 2019.

Rohlfing K, Leonardi G, Nomikou I, Rczaszek-Leonardi J, Hüllermeier E. Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems. Published online 2019. doi:10.1109/TCDS.2019.2892991