Melnikov, V., &; Hüllermeier, E. (2017). Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017 (pp. 1–12).

Shaker, A., Heldt, W., &; Hüllermeier, E. (2017). Learning TSK Fuzzy Rules from Data Streams. In Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Skopje, Macedonia.

Bräuning, M., Hüllermeier, E., Keller, T., &; Glaum, M. (2017). Lexicographic preferences for predictive modeling of human decision making. A new machine learning method with an application  in accounting. European Journal of Operational Research, 258(1), 295–306.

Platenius, M.-C., Shaker, A., Becker, M., Hüllermeier, E., &; Schäfer, W. (2017). Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic. IEEE Transactions on Software Engineering, 43(8), 739–759.

Hüllermeier, E. (2017). From Knowledge-based to Data-driven Modeling of Fuzzy Rule-based Systems: A Critical Reflection. The Computing Research Repository  (CoRR).

Ramaswamy, A., &; Bhatnagar, S. (2016). Stochastic recursive inclusion in two timescales with an application to the lagrangian dual problem. Stochastics, 88(8), 1173–1187.

Melnikov, V., Hüllermeier, E., Kaimann, D., Frick, B., &; Gupta,https://doi.org/10.4467/20838476si.16.006.6187__Pritha . (2016). Pairwise versus Pointwise Ranking: A Case Study. Schedae Informaticae, 25.https://doi.org/10.4467/20838476si.16.006.6187 

Platenius, M. C., Shaker, A., Becker, M., Hüllermeier, E., &; Schäfer, W. (2016). Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic. IEEE Transactions on Software Engineering (TSE), Presented at ICSE 2017, (8), 739–759.https://doi.org/10.1109/TSE.2016.2632115 

Melnikov, V., &; Hüllermeier, E. (2016). Learning to Aggregate Using Uninorms. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016) (pp. 756–771).https://doi.org/10.1007/978-3-319-46227-1_47 

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

Labreuche, C., Hüllermeier, E., Vojtas, P., &; Fallah Tehrani, A. (2016). On the identifiability of models  in multi-criteria preference learning. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, &; K. Pfannschmidt (Eds.), in Proceedings DPL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany.

Schäfer, D., &; Hüllermeier, E. (2016). Preference -based reinforcement learning using dyad ranking. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, &; K. Pfannschmidt (Eds.), in Proceedings DPL16 Euro Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn, Germany.

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

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

Schäfer, D., &; Hüllermeier, E. (2016). Plackett-Luce networks for dyad ranking. In in Workshop LWDA “Lernen, Wissen, Daten, Analysen” Potsdam, Germany.

Pfannschmidt, K., Hüllermeier, E., Held, S., &; Neiger, R. (2016). 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 (pp. 450–461). Springer.

Leinweber, M., Fober, T., Strickert, M., Baumgärtner, L., Klebe, G., Freisleben, B., &; Hüllermeier, E. (2016). CavSimBase: A database for large scale comparison of protein binding sites. IEEE Transactions on Knowledge and Data Engineering, 28(6), 1423–1434.

Mohr, F. (2016). Towards Automated Service Composition Under Quality Constraints. Universität Paderborn.https://doi.org/10.17619/UNIPB/1-171 

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

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