Wever, M. D., Mohr, F., &; Hüllermeier, E. (2018). Ensembles of Evolved Nested Dichotomies for Classification. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM.https://doi.org/10.1145/3205455.3205562 
LibreCat: /record/2109
DOI: 10.1145/3205455.3205562

Wever, M. D., Mohr, F., &; Hüllermeier, E. (2018). Automated Multi-Label Classification based on ML-Plan. Arxiv.
LibreCat: /record/17713

Mohr, F., Wever, M. D., &; Hüllermeier, E. (2018). Automated machine learning service composition.
LibreCat: /record/17714

Graf, H. (2018). Ranking of Classification Algorithms in AutoML. Universität Paderborn.
LibreCat: /record/5693

Scheibl, M. (2018). Learning about learning curves from dataset properties. Universität Paderborn.
LibreCat: /record/5936

Schäfer, D., &; Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. In Discovery Science (pp. 161–175). Cham: Springer International Publishing.https://doi.org/10.1007/978-3-030-01771-2_11 
LibreCat: /record/6423
DOI: 10.1007/978-3-030-01771-2_11

Abiteboul, S., Arenas, M., Barceló, P., Bienvenu, M., Calvanese, D., David, C.,  Yi, K. (Eds.). (2018). Research Directions for Principles of Data Management (Vol. 7, pp. 1–29).
LibreCat: /record/10591

Couso, I., &; Hüllermeier, E. (2018). Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In S. Mostaghim, A. Nürnberger, &; C. Borgelt (Eds.), Frontiers in Computational Intelligence (pp. 31–46). Springer.
LibreCat: /record/10783

Schäfer, D., &; Hüllermeier, E. (2018). Dyad ranking using Plackett-Luce models based on joint feature representations. Machine Learning, 107(5), 903–941.
LibreCat: /record/16038

Ahmadi Fahandar, M., &; Hüllermeier, E. (2018). Learning to Rank Based on Analogical Reasoning. In Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI) (pp. 2951–2958).
LibreCat: /record/10145