Calders, T., F. Esposito, Eyke Hüllermeier, and R. Meo. “Machine Learning and Knowledge Discovery in Databases-European Conf. ECML/PKDD, Nancy, France.” In Proceedings, Parts I-III. Lecture Notes in Computer Science, 8724–26. Springer, 2014.

Fürnkranz, J., Eyke Hüllermeier, Cynthia Rudin, Roman Slowinski, and Scott Sanner. “Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports,” 4:1–27, 2014.

Shaker, Ammar, and Eyke Hüllermeier. “Survival Analysis on Data Streams: Analyzing Temporal Events in Dynamically Changing Environments.” Applied Mathematics and Computer Science 24, no. 1 (2014): 199–212.

Hoffmann, F., Eyke Hüllermeier, and A. Kroll. “Ausgewählte Beiträge Des GMA-Fachausschusses 5.14.” Computational Intelligence Automatisierungstechnik 62, no. 10 (2014): 685–86.

Calders, T., F. Esposito, Eyke Hüllermeier, and R. Meo. “Guest Editors`introduction:Special Issue of the ECML/PKDD 2014 Journal Track.” Data Min. Knowledge Discovery 28, no. 5–6 (2014): 1129–33.

Henzgen, Sascha, M. Strickert, and Eyke Hüllermeier. “Visualization of Evolving Fuzzy Rule-Based Systems.” Evolving Systems 5, no. 3 (2014): 175–91.

Hüllermeier, Eyke. “Learning from Imprecise and Fuzzy Observations: Data Disambiguation through Generalized Loss Minimization.” Int. J. Approx. Reasoning 55, no. 7 (2014): 1519–34.

Hüllermeier, Eyke. “Rejoinder on "Learning from Imprecise and Fuzzy Observations: Data Disambiguation through Generalized Loss Minimization.” Int. J. Approx. Reasoning 55, no. 7 (2014): 1609–13.

Strickert, M., K. Bunte, F.- M. Schleif, and Eyke Hüllermeier. “Correlation-Based Embedding of Pairwise Score Data.” Neurocomputing 141 (2014): 97–109.

Senge, Robin, S. Bösner, K. Dembczynski, J. Haasenritter, O. Hirsch, N. Donner-Banzhoff, and Eyke Hüllermeier. “Reliable Classification: Learning Classifiers That Distinguish Aleatoric and Epistemic Uncertainty.” Information Sciences 255 (2014): 16–29.

Mernberger, M., M. Moog, S. Stork, S. Zauner, U.G. Maier, and Eyke Hüllermeier. “Protein Sub-Cellular Localization Prediction for Special Compartments via Optimized Time Series Distances.” J. Bioinformatics and Computational Biology 12, no. 1 (2014).

Calders, T., F. Esposito, Eyke Hüllermeier, and R. Meo. “Guest Editors`introduction:Special Issue of the ECML/PKDD 2014 Journal Track.” Machine Learning 97, no. 1–2 (2014): 1–3.

Busa-Fekete, Robert, B. Szörényi, P. Weng, W. Cheng, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning: Evolutionary Direct Policy Search Using a Preference-Based Racing Algorithm.” Machine Learning 97, no. 3 (2014): 327–51.

Montanés, E., Robin Senge, J. Barranquero, J.R. Quevedo, J.J. Del Coz, and Eyke Hüllermeier. “Dependent Binary Relevance Models for Multi-Label Classification.” Pattern Recognition 47, no. 3 (2014): 1494–1508.

Krempl, G., I. Zliobaite, D. Brzezinski, Eyke Hüllermeier, M. Last, V. Lemaire, T. Noack, et al. “Open Challenges for Data Stream Mining Research.” SIGKDD Explorations 16, no. 1 (2014): 1–10.

Krotzky, T., T. Fober, Eyke Hüllermeier, and G. Klebe. “Extended Graph-Based Models for Enhanced Similarity Search in Cavbase.” IEEE/ACM Trans. Comput. Biology Bioinform. 11, no. 5 (2014): 878–90.

Stock, M., T. Fober, Eyke Hüllermeier, S Glinca, G. Klebe, T. Pahikkala, A. Airola, B. De Baets, and W. Wageman. “Identification of Functionally Releated Enzymes by Learning to Rank Methods.” IEEE/ACM Trans. Comput. Biology Bioinform. 11, no. 6 (2014): 1157–69.

Beister, Frederic, Martin Dräxler, Jörg Aelken, and Holger Karl. “Power Model Design for ICT Systems – A Generic Approach.”https://doi.org/10.1016/j.comcom.2014.02.007__Computer Communications 50 (2014): 77–85.https://doi.org/10.1016/j.comcom.2014.02.007 .

Gharibian, Sevag, and Julia Kempe. “Hardness of Approximation for Quantum Problems.” Quantum Information &; Computation 14, no. 5–6 (2014): 517–40.

W. Berry, Dominic, Richard Cleve, and Sevag Gharibian. “Gate-Efficient Discrete Simulations of Continuous-Time Quantum Query Algorithms.” Quantum Information &; Computation 14, no. 1–2 (2014): 1–30.