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

El Mesaoudi-Paul, A., Hüllermeier, E., &; Busa-Fekete, R. (2018). Ranking Distributions based on Noisy Sorting. Proc. 35th Int. Conference on Machine Learning (ICML), 3469–3477.
LibreCat: /record/10148

Hesse, M., Timmermann, J., Hüllermeier, E., &; Trächtler, A. (2018). A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected Systems in Products and Production, Procedia Manufacturing 24, 15–20.
LibreCat: /record/10149

Mencia, E. L., Fürnkranz, J., Hüllermeier, E., &; Rapp, M. (2018). Learning interpretable rules for multi-label classification. In H. Jair Escalante, S. Escalera, I. Guyon, X. Baro, Y. Güclüütürk, U. Güclü, &; M. A. J. van Gerven (Eds.), Explainable and Interpretable Models in Computer Vision and Machine Learning (pp. 81–113). Springer.
LibreCat: /record/10152

Nguyen, V.-L., Destercke, S., Masson, M.-H., &; Hüllermeier, E. (2018). Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty. Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 5089–5095.
LibreCat: /record/10181

Schäfer, D., &; Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. Proc. 21st Int. Conference on Discovery Science (DS), 161–175.
LibreCat: /record/10184

Schäfer, D., &; Hüllermeier, E. (2018). Dyad Ranking Using Plackett-Luce Models based on joint feature representations. Machine Learning, 107(5), 903–941.
LibreCat: /record/10276

Seemann, N., Geierhos, M., Merten, M.-L., Tophinke, D., Wever, M. D., &; Hüllermeier, E. (2018). Supporting the Cognitive Process in Annotation Tasks. In K. Eckart &; D. Schlechtweg (Eds.), Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft.
LibreCat: /record/1379

Ramaswamy, A., &; Bhatnagar, S. (2017). Analysis of gradient descent methods with nondiminishing bounded errors. IEEE Transactions on Automatic Control, 63(5), 1465–1471.
LibreCat: /record/24152

Ramaswamy, A., &; Bhatnagar, S. (2017). A generalization of the Borkar-Meyn theorem for stochastic recursive inclusions. Mathematics of Operations Research, 42(3), 648–661.
LibreCat: /record/24153

Melnikov, V., &; Hüllermeier, E. (2017). Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. KIT Scientific Publishing.https://doi.org/10.5445/KSP/1000074341 
LibreCat: /record/3325
DOI: 10.5445/KSP/1000074341