Heid, S. H., Wever, M. D., &; Hüllermeier, E. (n.d.). Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. In Journal of Data Mining and Digital Humanities. episciences.
LibreCat: /record/17605

Tornede, A., Wever, M. D., &; Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. Workshop MetaLearn 2020 @ NeurIPS 2020. Workshop MetaLearn 2020 @ NeurIPS 2020, Online.
LibreCat: /record/20306

El Mesaoudi-Paul, A., Weiß, D., Bengs, V., Hüllermeier, E., &; Tierney, K. (2020). Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In Learning and Intelligent Optimization. LION 2020. (Vol. 12096, pp. 216–232). Cham: Springer.https://doi.org/10.1007/978-3-030-53552-0_22 
LibreCat: /record/18014
DOI: 10.1007/978-3-030-53552-0_22

El Mesaoudi-Paul, A., Bengs, V., &; Hüllermeier, E. (n.d.). Online Preselection with Context Information under the Plackett-Luce  Model. ArXiv:2002.04275.
LibreCat: /record/18017

Tornede, A., Wever, M. D., Werner, S., Mohr, F., &; Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. ACML 2020. 12th Asian Conference on Machine Learning, Bangkok, Thailand.
LibreCat: /record/18276

Richter, C., Hüllermeier, E., Jakobs, M.-C., &; Wehrheim, H. (n.d.). Algorithm Selection for Software Validation Based on Graph Kernels. Journal of Automated Software Engineering.
LibreCat: /record/16725

Wever, M. D., Tornede, A., Mohr, F., &; Hüllermeier, E. (n.d.). LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Symposium on Intelligent Data Analysis, Konstanz, Germany.
LibreCat: /record/15629

Wever, M. D., van Rooijen, L., &; Hamann, H. (2020). Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary Computation, 28(2), 165–193.https://doi.org/10.1162/evco_a_00266 
LibreCat: /record/15025
DOI: 10.1162/evco_a_00266

Pfannschmidt, K., Gupta, P., &; Hüllermeier, E. (2019). Learning Choice Functions: Concepts and Architectures. ArXiv:1901.10860.
LibreCat: /record/19523

Merten, M.-L., Seemann, N., &; Wever, M. D. (2019). Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. Niederdeutsches Jahrbuch, 142, 124–146.
LibreCat: /record/17565

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