Bengs, V., &; Holzmann, H. (2019). Uniform approximation in classical weak convergence theory. ArXiv:1903.09864.
LibreCat: /record/18018

Wever, M. D., Mohr, F., Hüllermeier, E., &; Hetzer, A. (2019). Towards Automated Machine Learning for Multi-Label Classification. Presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany.
LibreCat: /record/8868

Tagne, V. K., Fotso, S., Fono, L. A., &; Hüllermeier, E. (2019). Choice Functions Generated by Mallows and Plackett–Luce Relations. New Mathematics and Natural Computation, 15(2), 191–213.
LibreCat: /record/10578

Couso, I., Borgelt, C., Hüllermeier, E., &; Kruse, R. (2019). Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Computational Intelligence Magazine, 31–44.https://doi.org/10.1109/mci.2018.2881642 
LibreCat: /record/15001
DOI: 10.1109/mci.2018.2881642

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 
LibreCat: /record/15002
DOI: 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.
LibreCat: /record/15003

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 
LibreCat: /record/15004
DOI: 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 
LibreCat: /record/15005
DOI: 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 
LibreCat: /record/15006
DOI: 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 
LibreCat: /record/15007
DOI: 10.1016/j.jmva.2019.02.017