Agarwal M, Fallah Tehrani A, Hüllermeier E. Preference-based learning of ideal solutions in TOPSIS-like decision models. Journal of Multi-Criteria Decision Analysis. 2014;22(3-4).

Krotzky T, Fober T, Hüllermeier E, Klebe G. Extended graph-based models for enhanced similarity search in Cabase. IEEE/ACM Transactions of Computational Biology and Bioinformatics. 2014;11(5):878-890.

Hüllermeier E. Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. International Journal of Approximate Reasoning. 2014;55(7):1519-1534.

Henzgen S, Strickert M, Hüllermeier E. Visualization of evolving fuzzy-rule-based systems. Evolving Systems. 2014;5:175-191.

Busa-Fekete R, Szörenyi B, Weng P, Cheng W, Hüllermeier E. Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm. Machine Learning. 2014;97(3):327-351.

Krempl G, Zliobaite I, Brzezinski D, et al. Open challenges for data stream mining research. SIGKDD Explorations. 2014;16(1):1-10.

Strickert M, Bunte K, Schleif FM, Hüllermeier E. Correlation-based embedding of pairwise score data. Neurocomputing. 2014;141:97-109.

Shaker A, Hüllermeier E. Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. International Journal of Applied Mathematics and Computer Science. 2014;24(1):199-212.

Senge R, Bösner S, Dembczynski K, et al. Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences. 2014;255:16-29.

Donner-Banzhoff N, Haasenritter J, Hüllermeier E, Viniol A, Bösner S, Becker A. The comprehensive diagnostic study is suggested as a design to model the diagnostic process. Journal of Clinical Epidemiology. 2014;2(67):124-132.

Lukovszki T, Meyer auf der Heide F. Fast Collisionless Pattern Formation by Anonymous, Position-Aware Robots. In: Lecture Notes in Computer Science. Cham; 2014. doi:10.1007/978-3-319-14472-6_17

Abshoff S, Meyer auf der Heide F. Continuous Aggregation in Dynamic Ad-Hoc Networks. In: Structural Information and Communication Complexity. Cham; 2014. doi:10.1007/978-3-319-09620-9_16

Graf T, Platzner M. Common Fate Graph Patterns in Monte Carlo Tree Search for Computer Go. In: 2014 IEEE Conference on Computational Intelligence and Games. ; 2014:1-8. doi:10.1109/CIG.2014.6932863

Busa-Fekete R, Szörényi B, Hüllermeier E. PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences. In: Proceedings AAAI 2014, Quebec, Canada. ; 2014:1701-1707.

Busa-Fekete R, Hüllermeier E. A Survey of Preference-Based Online Learning with Bandit Algorithms. In: Proceedings Int. Conf. on Algorithmic Learning Theory (ALT), Bled, Slovenia. ; 2014:18-39.

Henzgen S, Hüllermeier E. Mining Rank Data. In: Proceedings Discovery Science, Bled,Slovenia . ; 2014:123-134.

Fallah Tehrani A, Strickert M, Hüllermeier E. The Choquet kernel for monotone data. In: Proceedings ESANN , Bruges, Belgium. ; 2014.

Abdel-Aziz A, Strickert M, Hüllermeier E. Learning Solution Similarity in Preference-Based CBR. In: Proceedings Int. Conf. Case-Based Reasoning (ICCBR), Cork, Ireland. ; 2014:17-31.

Schäfer D, Hüllermeier E. Dyad Ranking Using A Bilinear Plackett-Luce Model. In: Proceedings Lernen-Wissensentdeckung-Adaptivität (LWA), Aachen, Germany. ; 2014:32-33.

Calders T, Esposito F, Hüllermeier E, Meo R. Machine Learning and Knowledge Discovery in Databases-European Conf. ECML/PKDD, Nancy, France. In: Proceedings, Parts I-III. Lecture Notes in Computer Science. Springer; 2014:8724-8726.