Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Ensembles of Evolved Nested Dichotomies for Classification.”https://doi.org/10.1145/3205455.3205562__In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM, 2018.https://doi.org/10.1145/3205455.3205562 .

Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automated Multi-Label Classification Based on ML-Plan.” Arxiv, 2018.

Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “Automated Machine Learning Service Composition,” 2018.

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

Scheibl, Manuel. Learning about Learning Curves from Dataset Properties. Universität Paderborn, 2018.

Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning Using Dyad Ranking.”https://doi.org/10.1007/978-3-030-01771-2_11__In Discovery Science, 161–75. Cham: Springer International Publishing, 2018.https://doi.org/10.1007/978-3-030-01771-2_11 .

Abiteboul, S., M. Arenas, P. Barceló, M. Bienvenu, D. Calvanese, C. David, R. Hull, et al., eds. Research Directions for Principles of Data Management. Vol. 7, 2018.

Couso, Ines, and Eyke Hüllermeier. “Statistical Inference for Incomplete Ranking Data: A Comparison of Two Likelihood-Based Estimators.” In Frontiers in Computational Intelligence, edited by Sanaz Mostaghim, Andreas Nürnberger, and Christian Borgelt, 31–46. Springer, 2018.

Schäfer, D., and Eyke Hüllermeier. “Dyad Ranking Using Plackett-Luce Models Based on Joint Feature Representations.” Machine Learning 107, no. 5 (2018): 903–41.

Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Learning to Rank Based on Analogical Reasoning.” In Proc. 32 Nd AAAI Conference on Artificial Intelligence (AAAI), 2951–58, 2018.

El Mesaoudi-Paul, Adil, Eyke Hüllermeier, and Robert Busa-Fekete. “Ranking Distributions Based on Noisy Sorting.” In Proc. 35th Int. Conference on Machine Learning (ICML), 3469–77. Verlagsschriftenreihe Des Heinz Nixdorf Instituts, Paderborn. Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn, 2018.

Hesse, M., J. Timmermann, Eyke Hüllermeier, and Ansgar 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, 15–20, 2018.

Mencia, E.Loza, J. Fürnkranz, Eyke Hüllermeier, and M. Rapp. “Learning Interpretable Rules for Multi-Label Classification.” In Explainable and Interpretable Models in Computer Vision and Machine Learning, edited by H. Jair Escalante, S. Escalera, I. Guyon, X. Baro, Y. Güclüütürk, U. Güclü, and M.A.J. van Gerven, 81–113. The Springer Series on Challenges in Machine Learning. Springer, 2018.

Nguyen, Vu-Linh, Sebastian Destercke, M.-H. Masson, and Eyke Hüllermeier. “Reliable Multi-Class Classification Based on Pairwise Epistemic and Aleatoric Uncertainty.” In Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 5089–95, 2018.

Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning Using Dyad Ranking.” In Proc. 21st Int. Conference on Discovery Science (DS), 161–75, 2018.

Schäfer, Dirk, and Eyke Hüllermeier. “Dyad Ranking Using Plackett-Luce Models Based on Joint Feature Representations.” Machine Learning 107, no. 5 (2018): 903–41.

Seemann, Nina, Michaela Geierhos, Marie-Luis Merten, Doris Tophinke, Marcel Dominik Wever, and Eyke Hüllermeier. “Supporting the Cognitive Process in Annotation Tasks.” In Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft, edited by Kerstin  Eckart and Dominik  Schlechtweg, 2018.

Ramaswamy, Arunselvan, and Shalabh Bhatnagar. “Analysis of Gradient Descent Methods with Nondiminishing Bounded Errors.” IEEE Transactions on Automatic Control 63, no. 5 (2017): 1465–71.

Ramaswamy, Arunselvan, and Shalabh Bhatnagar. “A Generalization of the Borkar-Meyn Theorem for Stochastic Recursive Inclusions.” Mathematics of Operations Research 42, no. 3 (2017): 648–61.

Melnikov, Vitalik, and Eyke Hüllermeier. “Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics.”https://doi.org/10.5445/KSP/1000074341__In Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. KIT Scientific Publishing, 2017.https://doi.org/10.5445/KSP/1000074341 .