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

Krempl, G., Zliobaite, I., Brzezinski, D., Hüllermeier, E., Last, M., Lemaire, V.,  Stefanowski, J. (2014). Open challenges for data stream mining research. SIGKDD Explorations, 16(1), 1–10.

Strickert, M., Bunte, K., Schleif, F. M., &; Hüllermeier, E. (2014). Correlation-based embedding of pairwise score data. Neurocomputing, 141, 97–109.

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

Senge, R., Bösner, S., Dembczynski, K., Haasenritter, J., Hirsch, O., Donner-Banzhoff, N., &; Hüllermeier, E. (2014). Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences, 255, 16–29.

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

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

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

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

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

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

Henzgen, S., &; Hüllermeier, E. (2014). Mining Rank Data. In Proceedings Discovery Science, Bled,Slovenia  (pp. 123–134).

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

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

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

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

Fürnkranz, J., Hüllermeier, E., Rudin, C., Slowinski, R., &; Sanner, S. (2014). Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports (Vol. 4, pp. 1–27).

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

Hoffmann, F., Hüllermeier, E., &; Kroll, A. (2014). Ausgewählte Beiträge des GMA-Fachausschusses 5.14. Computational Intelligence Automatisierungstechnik, 62(10), 685–686.

Calders, T., Esposito, F., Hüllermeier, E., &; Meo, R. (2014). Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track. Data Min. Knowledge Discovery, 28(5–6), 1129–1133.