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.

Henzgen, S., Strickert, M., &; Hüllermeier, E. (2014). Visualization of evolving fuzzy rule-based systems. Evolving Systems, 5(3), 175–191.

Hüllermeier, E. (2014). Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int. J. Approx. Reasoning, 55(7), 1519–1534.

Hüllermeier, E. (2014). Rejoinder on "Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int. J. Approx. Reasoning, 55(7), 1609–1613.

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

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.

Mernberger, M., Moog, M., Stork, S., Zauner, S., Maier, U. G., &; Hüllermeier, E. (2014). Protein Sub-Cellular Localization Prediction for Special compartments via Optimized Time Series Distances. J. Bioinformatics and Computational Biology, 12(1).

Calders, T., Esposito, F., Hüllermeier, E., &; Meo, R. (2014). Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track. Machine Learning, 97(1–2), 1–3.

Busa-Fekete, R., Szörényi, 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.

Montanés, E., Senge, R., Barranquero, J., Quevedo, J. R., Del Coz, J. J., &; Hüllermeier, E. (2014). Dependent binary relevance models for multi-label classification. Pattern Recognition, 47(3), 1494–1508.

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.

Krotzky, T., Fober, T., Hüllermeier, E., &; Klebe, G. (2014). Extended Graph-Based Models for Enhanced Similarity Search in Cavbase. IEEE/ACM Trans. Comput. Biology Bioinform., 11(5), 878–890.

Stock, M., Fober, T., Hüllermeier, E., Glinca, S., Klebe, G., Pahikkala, T.,  Wageman, W. (2014). Identification of Functionally Releated Enzymes by Learning to Rank Methods. IEEE/ACM Trans. Comput. Biology Bioinform., 11(6), 1157–1169.

Beister, F., Dräxler, M., Aelken, J., &; Karl, H. (2014). Power model design for ICT systems – A generic approach. Computer Communications, 50, 77–85.https://doi.org/10.1016/j.comcom.2014.02.007 

Gharibian, S., &; Kempe, J. (2014). Hardness of approximation for quantum problems. Quantum Information &; Computation, 14(5–6), 517–540.

W. Berry, D., Cleve, R., &; Gharibian, S. (2014). Gate-efficient discrete simulations of continuous-time quantum query algorithms. Quantum Information &; Computation, 14(1–2), 1–30.