Ensembles of Evolved Nested Dichotomies for Classification
M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, Kyoto, Japan, 2018.

Automated Multi-Label Classification based on ML-Plan
M.D. Wever, F. Mohr, E. Hüllermeier, (2018).

Automated machine learning service composition
F. Mohr, M.D. Wever, E. Hüllermeier, (2018).

Ranking of Classification Algorithms in AutoML
H. Graf, Ranking of Classification Algorithms in AutoML, Universität Paderborn, 2018.

Learning about learning curves from dataset properties
M. Scheibl, Learning about Learning Curves from Dataset Properties, Universität Paderborn, 2018.

Preference-Based Reinforcement Learning Using Dyad Ranking
D. Schäfer, E. Hüllermeier, in: Discovery Science, Springer International Publishing, Cham, 2018, pp. 161–175.

Research Directions for Principles of Data Management
S. Abiteboul, M. Arenas, P. Barceló, M. Bienvenu, D. Calvanese, C. David, R. Hull, E. Hüllermeier, B. Kimelfeld, L. Libkin, W. Martens, T. Milo, F. Murlak, F. Neven, M. Ortiz, T. Schwentick, J. Stoyanovich, J. Su, D. Suciu, V. Vianu, K. Yi, eds., Research Directions for Principles of Data Management, 2018.

Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators
I. Couso, E. Hüllermeier, in: S. Mostaghim, A. Nürnberger, C. Borgelt (Eds.), Frontiers in Computational Intelligence, Springer, 2018, pp. 31–46.

Dyad ranking using Plackett-Luce models based on joint feature representations
D. Schäfer, E. Hüllermeier, Machine Learning 107 (2018) 903–941.

Learning to Rank Based on Analogical Reasoning
M. Ahmadi Fahandar, E. Hüllermeier, in: Proc. 32 Nd AAAI Conference on Artificial Intelligence (AAAI), 2018, pp. 2951–2958.

Ranking Distributions based on Noisy Sorting
A. El Mesaoudi-Paul, E. Hüllermeier, R. Busa-Fekete, in: Proc. 35th Int. Conference on Machine Learning (ICML), Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn, 2018, pp. 3469–3477.

A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart
M. Hesse, J. Timmermann, E. Hüllermeier, A. Trächtler, in: Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected Systems in Products and Production, Procedia Manufacturing 24, 2018, pp. 15–20.

Learning interpretable rules for multi-label classification
E.L. Mencia, J. Fürnkranz, E. Hüllermeier, M. Rapp, in: H. Jair Escalante, S. Escalera, I. Guyon, X. Baro, Y. Güclüütürk, U. Güclü, M.A.J. van Gerven (Eds.), Explainable and Interpretable Models in Computer Vision and Machine Learning, Springer, 2018, pp. 81–113.

Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty
V.-L. Nguyen, S. Destercke, M.-H. Masson, E. Hüllermeier, in: Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 2018, pp. 5089–5095.

Preference-Based Reinforcement Learning Using Dyad Ranking
D. Schäfer, E. Hüllermeier, in: Proc. 21st Int. Conference on Discovery Science (DS), 2018, pp. 161–175.

Dyad Ranking Using Plackett-Luce Models based on joint feature representations
D. Schäfer, E. Hüllermeier, Machine Learning 107 (2018) 903–941.

Supporting the Cognitive Process in Annotation Tasks
N. Seemann, M. Geierhos, M.-L. Merten, D. Tophinke, M.D. Wever, E. Hüllermeier, in: K. Eckart, D. Schlechtweg (Eds.), Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft, 2018.

Analysis of gradient descent methods with nondiminishing bounded errors
A. Ramaswamy, S. Bhatnagar, IEEE Transactions on Automatic Control 63 (2017) 1465–1471.

A generalization of the Borkar-Meyn theorem for stochastic recursive inclusions
A. Ramaswamy, S. Bhatnagar, Mathematics of Operations Research 42 (2017) 648–661.

Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics
V. Melnikov, E. Hüllermeier, in: Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017, KIT Scientific Publishing, 2017.

Certiû01cation Matters for Service Markets
M.-C. Jakobs, J. Krämer, D. van Straaten, T. Lettmann, in: T.P. Marcelo De Barros, Janusz Klink,Tadeus Uhl (Ed.), The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION), 2017, pp. 7–12.

Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German
N. Seemann, M.-L. Merten, M. Geierhos, D. Tophinke, E. Hüllermeier, in: Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, Association for Computational Linguistics (ACL), Stroudsburg, PA, USA, 2017, pp. 40–45.

Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies
N.N. Schnitker, Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies, Universität Paderborn, 2017.

jPL: A Java-based Software Framework for Preference Learning
P. Gupta, A. Hetzer, T. Tornede, S. Gottschalk, A. Kornelsen, S. Osterbrink, K. Pfannschmidt, E. Hüllermeier, in: 2017.

Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction
A. Hetzer, T. Tornede, Solving the Container Pre-Marshalling Problem Using Reinforcement Learning and Structured Output Prediction, Universität Paderborn, 2017.

Predicting Rankings of Software Verification Tools
M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, in: Proceedings of the 3rd International Workshop on Software Analytics, 2017, pp. 23–26.

Predicting Rankings of Software Verification Competitions
M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, Predicting Rankings of Software Verification Competitions, 2017.

Preference Learning
J. Fürnkranz, E. Hüllermeier, in: Encyclopedia of Machine Learning and Data Mining, 2017, pp. 1000–1005.

Preference Learning
J. Fürnkranz, E. Hüllermeier, in: C. Sammut, G.I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining, Springer, 2017, pp. 1000–1005.

Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization
M.D. Wever, F. Mohr, E. Hüllermeier, in: 27th Workshop Computational Intelligence, Dortmund, 2017.

Optimizing the structure of nested dichotomies. A comparison of two heuristics
V. Melnikov, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier, R. Mikut (Eds.), In Proceedings 27th Workshop Computational Intelligence, Dortmund Germany, KIT Scientific Publishing, 2017, pp. 1–12.

Predicting rankings of software verification tools
M. Czech, E. Hüllermeier, M.C. Jacobs, H. Wehrheim, in: In Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany, 2017.

Maximum likelihood estimation and coarse data
I. Couso, D. Dubois, E. Hüllermeier, in: In Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain, Springer, 2017, pp. 3–16.

Estimating relative depth in single images via rankboost
R. Ewerth, M. Springstein, E. Müller, A. Balz, J. Gehlhaar, T. Naziyok, K. Dembczynski, E. Hüllermeier, in: Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017), 2017, pp. 919–924.

Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening
M. Ahmadi Fahandar, E. Hüllermeier, I. Couso, in: Proc. 34th Int. Conf. on Machine Learning (ICML 2017), 2017, pp. 1078–1087.

Planning with Independent Task Networks
F. Mohr, T. Lettmann, E. Hüllermeier, in: Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017), 2017, pp. 193–206.

Predicting rankings of software verification tools
M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, in: Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017, 2017, pp. 23–26.

Maximum Likelihood Estimation and Coarse Data
I. Couso, D. Dubois, E. Hüllermeier, in: Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017), 2017, pp. 3–16.

Learning to Rank based on Analogical Reasoning
M. Ahmadi Fahandar, E. Hüllermeier, in: Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence, 2017.

(Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017
F. Hoffmann, E. Hüllermeier, R. Mikut, in: 2017.

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

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

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

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

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

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

Pairwise versus Pointwise Ranking: A Case Study
V. Melnikov, E. Hüllermeier, D. Kaimann, B. Frick,  Pritha  Gupta, Schedae Informaticae 25 (2016).

Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic
M.C. Platenius, A. Shaker, M. Becker, E. Hüllermeier, W. Schäfer, IEEE Transactions on Software Engineering (TSE), Presented at ICSE 2017 (2016) 739–759.

Learning to Aggregate Using Uninorms
V. Melnikov, E. Hüllermeier, in: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016), 2016, pp. 756–771.

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