E. Moritzer, M. Hüttner, B. Henning, and M. Webersen, “Non-destructive characterization of hygrothermally aged polymers,” presented at the ANTEC 2016, Indianapolis, 2016.

E. Moritzer, M. Hüttner, B. Henning, and M. Webersen, “Ultraschallbasierte Charakterisierung von gealterten Polymeren,” in Jahresmagazin Kunststofftechnik 2016, Institut für Wissenschaftliche Veröffentlichungen, Ed. 2016, pp. 2–7.

M. Webersen, M. Hüttner, F. Bause, E. Moritzer, and B. Henning, “Zerstörungsfreie Charakterisierung des hydrothermischen Alterungsverhaltens von Polymeren,” presented at the 18. GMA/ITG-Fachtagung Sensoren und Messsysteme 2016, Nürnberg, 2016, pp. 683–688.

M. Dewender, Verifikation von Service Kompositionen mit Spin. Universität Paderborn, 2016.

P. Heinisch, Verifikation von Service Kompositionen mit Prolog. Universität Paderborn, 2016.

M. Eikel, Insider-resistent Distributed Storage Systems. Universität Paderborn, 2016.

J. Fürnkranz and E. Hüllermeier, “Preference Learning,” in Encyclopedia of Machine Learning and Data Mining, C. Sammut and G. I. Webb, Eds. Springer, 2016.

F. Hoffmann, E. Hüllermeier, and R. Mikut, Eds.,  Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany. 2016.

K. Jasinska, K. Dembczynski, R. Busa-Fekete, T. Klerx, and E. Hüllermeier, “Extreme F-measure maximization using sparse probability estimates ,” in Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA, 2016.

V. Melnikov and E. Hüllermeier, “Learning to aggregate using uninorms,  in Proceedings ECML/PKDD-2016,” in European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy, 2016, pp. 756–771.

K. Dembczynski, W. Kotlowski, W. Waegeman, R. Busa-Fekete, and E. Hüllermeier, “Consistency of probalistic classifier trees,” in In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy, 2016, pp. 511–526.

A. Shabani, A. Paul, R. Platon, and E. Hüllermeier, “Predicting the electricity consumption of buildings: An improved CBR approach,” in In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA, 2016, pp. 356–369.

K. Pfannschmidt, E. Hüllermeier, S. Held, and R. Neiger, “Evaluating tests in medical  diagnosis-Combining machine learning with game-theoretical concepts,” in In Proceedings IPMU 16th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands, 2016, pp. 450–461.

C. Labreuche, E. Hüllermeier, P. Vojtas, and A. Fallah Tehrani, “On the Identifiability of models in multi-criteria preference learning ,” in Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, 2016.

D. Schäfer and E. Hüllermeier, “Preference-Based Reinforcement Learning Using Dyad Ranking,” in Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, 2016.

I. Couso, M. Ahmadi Fahandar, and E. Hüllermeier, “Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators,” in Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, 2016.

S. Lu and E. Hüllermeier, “Support vector classification on noisy data using fuzzy supersets losses,” in Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing, 2016, pp. 1–8.

D. Schäfer and E. Hüllermeier, “Plackett-Luce networks for dyad ranking,” in In Workshop LWDA “Lernen, Wissen, Daten, Analysen,” 2016.

G. A. Kaminka et al., Eds., ECAI 2016, 22nd European Conference on Artificial Intelligence, including PAIS 2016, Prestigious Applications of Artificial Intelligence, vol. 285. The Hague, The Netherlands: IOS Press, 2016.

M. Leinweber et al., “CavSimBase: A database for large scale comparison of protein binding sites,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 6, pp. 1423–1434, 2016.