M. D. Wever, F. Mohr, and E. Hüllermeier, “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, 2018.

M. D. Wever, F. Mohr, and E. Hüllermeier, “Automated Multi-Label Classification based on ML-Plan.” Arxiv, 2018.

F. Mohr, M. D. Wever, and E. Hüllermeier, “Automated machine learning service composition.” 2018.

H. Graf, Ranking of Classification Algorithms in AutoML. Universität Paderborn, 2018.

M. Scheibl, Learning about learning curves from dataset properties. Universität Paderborn, 2018.

D. Schäfer and E. Hüllermeier, “Preference-Based Reinforcement Learning Using Dyad Ranking,” in Discovery Science, Cham: Springer International Publishing, 2018, pp. 161–175.

S. Abiteboul et al., Eds., Research Directions for Principles of Data Management, vol. 7, no. 1. 2018, pp. 1–29.

I. Couso and E. Hüllermeier, “Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators,” in Frontiers in Computational Intelligence, S. Mostaghim, A. Nürnberger, and C. Borgelt, Eds. Springer, 2018, pp. 31–46.

D. Schäfer and E. Hüllermeier, “Dyad ranking using Plackett-Luce models based on joint feature representations,” Machine Learning, vol. 107, no. 5, pp. 903–941, 2018.

M. Ahmadi Fahandar and E. Hüllermeier, “Learning to Rank Based on Analogical Reasoning,” in Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI), 2018, pp. 2951–2958.

A. El Mesaoudi-Paul, E. Hüllermeier, and R. Busa-Fekete, “Ranking Distributions based on Noisy Sorting,” in Proc. 35th Int. Conference on Machine Learning (ICML), 2018, pp. 3469–3477.

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

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

V.-L. Nguyen, S. Destercke, M.-H. Masson, and E. Hüllermeier, “Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty,” in Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 2018, pp. 5089–5095.

D. Schäfer and E. Hüllermeier, “Preference-Based Reinforcement Learning Using Dyad Ranking,” in Proc. 21st Int. Conference on Discovery Science (DS), 2018, pp. 161–175.

D. Schäfer and E. Hüllermeier, “Dyad Ranking Using Plackett-Luce Models based on joint feature representations,” Machine Learning, vol. 107, no. 5, pp. 903–941, 2018.

N. Seemann, M. Geierhos, M.-L. Merten, D. Tophinke, M. D. Wever, and E. Hüllermeier, “Supporting the Cognitive Process in Annotation Tasks,” in Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft, Stuttgart, Germany, 2018.

A. Ramaswamy and S. Bhatnagar, “Analysis of gradient descent methods with nondiminishing bounded errors,” IEEE Transactions on Automatic Control, vol. 63, no. 5, pp. 1465–1471, 2017.

A. Ramaswamy and S. Bhatnagar, “A generalization of the Borkar-Meyn theorem for stochastic recursive inclusions,” Mathematics of Operations Research, vol. 42, no. 3, pp. 648–661, 2017.

V. Melnikov and E. Hüllermeier, “Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics,” in Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017, 2017.