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

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

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

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

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

Extreme F-measure maximization using sparse probability estimates 
K. Jasinska, K. Dembczynski, R. Busa-Fekete, T. Klerx, E. Hüllermeier, in: M.F. Balcan, K.Q. Weinberger (Eds.), Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA, 2016.

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

Consistency of probalistic classifier trees
K. Dembczynski, W. Kotlowski, W. Waegeman, R. Busa-Fekete, E. Hüllermeier, 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.

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

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

On the Identifiability of models in multi-criteria preference learning 
C. Labreuche, E. Hüllermeier, P. Vojtas, A. Fallah Tehrani, in: R. Busa-Fekete, E. Hüllermeier, V. Mousseau, K. Pfannschmidt (Eds.), Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, 2016.

Preference-Based Reinforcement Learning Using Dyad Ranking
D. Schäfer, E. Hüllermeier, in: R. Busa-Fekete, E. Hüllermeier, V. Mousseau, K. Pfannschmidt (Eds.), Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, 2016.

Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators
I. Couso, M. Ahmadi Fahandar, E. Hüllermeier, in: R. Busa-Fekete, E. Hüllermeier, V. Mousseau, K. Pfannschmidt (Eds.), Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, 2016.

Support vector classification on noisy data using fuzzy supersets losses
S. Lu, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier, R. Mikut (Eds.), Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing, 2016, pp. 1–8.

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

ECAI 2016, 22nd European Conference on Artificial Intelligence, including PAIS 2016, Prestigious Applications of Artificial Intelligence
G.A. Kaminka, M. Fox, P. Bouquet, E. Hüllermeier, V. Dignum, F. Dignum, F. van Harmelen, eds., ECAI 2016, 22nd European Conference on Artificial Intelligence, Including PAIS 2016, Prestigious Applications of Artificial Intelligence, IOS Press, The Hague, The Netherlands, 2016.

CavSimBase: A database for large scale comparison of protein binding sites
M. Leinweber, T. Fober, M. Strickert, L. Baumgärtner, G. Klebe, B. Freisleben, E. Hüllermeier, IEEE Transactions on Knowledge and Data Engineering 28 (2016) 1423–1434.

Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification
M. Riemenschneider, R. Senge, U. Neumann, E. Hüllermeier, D. Heider, BioData Mining 9 (2016).

Delayed (de-)activation in servers with a sleep mode
M. Herlich, N. Bredenbals, H. Karl, Sustainable Computing: Informatics and Systems 10 (2016) 48–55.

Fast Dynamic Fault Injection for Virtual Microcontroller Platforms
P. Adelt, B. Koppelmann, W. Müller, M. Becker, B. Kleinjohann, C. Scheytt, in: Proceedings of the IEEE/IFIP International Conference on VLSI (VLSI-SOC), Tallin, Estonia, 2016.