Mohr, F., Wever, M. D., Hüllermeier, E., &; Faez, A. (2018). (WIP) Towards the Automated Composition of Machine Learning Services. In SCC. San Francisco, CA, USA: IEEE.https://doi.org/10.1109/SCC.2018.00039 

Pfannschmidt, K., Gupta, P., &; Hüllermeier, E. (2018). Deep Architectures for Learning Context-dependent Ranking Functions. ArXiv:1803.05796.

Mohr, F., Lettmann, T., Hüllermeier, E., &; Wever, M. D. (2018). Programmatic Task Network Planning. In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (pp. 31–39). Delft, Netherlands: AAAI.

Ramaswamy, A., &; Bhatnagar, S. (2018). Stability of stochastic approximations with “controlled markov” noise and temporal difference learning. IEEE Transactions on Automatic Control, 64(6), 2614–2620.

Demirel, B., Ramaswamy, A., Quevedo, D. E., &; Karl, H. (2018). Deepcas: A deep reinforcement learning algorithm for control-aware scheduling. IEEE Control Systems Letters, 2(4), 737–742.

Mohr, F., Wever, M. D., &; Hüllermeier, E. (2018). On-The-Fly Service Construction with Prototypes. In SCC. San Francisco, CA, USA: IEEE Computer Society.https://doi.org/10.1109/SCC.2018.00036 

Melnikov, V., &; Hüllermeier, E. (2018). On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning.https://doi.org/10.1007/s10994-018-5733-1 

Mohr, F., Wever, M. D., &; Hüllermeier, E. (2018). ML-Plan: Automated Machine Learning via Hierarchical Planning. Machine Learning, 1495–1515.https://doi.org/10.1007/s10994-018-5735-z 

Mohr, F., Wever, M. D., &; Hüllermeier, E. (n.d.). Reduction Stumps for Multi-Class Classification. In Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands.https://doi.org/10.1007/978-3-030-01768-2_19 

Wever, M. D., Mohr, F., &; Hüllermeier, E. (2018). ML-Plan for Unlimited-Length Machine Learning Pipelines. In ICML 2018 AutoML Workshop. Stockholm, Sweden.

Wever, M. D., Mohr, F., &; Hüllermeier, E. (2018). Ensembles of Evolved Nested Dichotomies for Classification. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM.https://doi.org/10.1145/3205455.3205562 

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

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

Scheibl, M. (2018). Learning about learning curves from dataset properties. Universität Paderborn.

Schäfer, D., &; Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. In Discovery Science (pp. 161–175). Cham: Springer International Publishing.https://doi.org/10.1007/978-3-030-01771-2_11 

Abiteboul, S., Arenas, M., Barceló, P., Bienvenu, M., Calvanese, D., David, C.,  Yi, K. (Eds.). (2018). Research Directions for Principles of Data Management (Vol. 7, pp. 1–29).

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

Schäfer, D., &; Hüllermeier, E. (2018). Dyad ranking using Plackett-Luce models based on joint feature representations. Machine Learning, 107(5), 903–941.

Ahmadi Fahandar, M., &; Hüllermeier, E. (2018). Learning to Rank Based on Analogical Reasoning. In Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI) (pp. 2951–2958).

El Mesaoudi-Paul, A., Hüllermeier, E., &; Busa-Fekete, R. (2018). Ranking Distributions based on Noisy Sorting. Proc. 35th Int. Conference on Machine Learning (ICML), 3469–3477.

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

Mencia, E. L., Fürnkranz, J., Hüllermeier, E., &; Rapp, M. (2018). Learning interpretable rules for multi-label classification. 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 (pp. 81–113). Springer.

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

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

Schäfer, D., &; Hüllermeier, E. (2018). Dyad Ranking Using Plackett-Luce Models based on joint feature representations. Machine Learning, 107(5), 903–941.

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

Ramaswamy, A., &; Bhatnagar, S. (2017). Analysis of gradient descent methods with nondiminishing bounded errors. IEEE Transactions on Automatic Control, 63(5), 1465–1471.

Ramaswamy, A., &; Bhatnagar, S. (2017). A generalization of the Borkar-Meyn theorem for stochastic recursive inclusions. Mathematics of Operations Research, 42(3), 648–661.

Melnikov, V., &; Hüllermeier, E. (2017). Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. KIT Scientific Publishing.https://doi.org/10.5445/KSP/1000074341 

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Seemann, N., Merten, M.-L., Geierhos, M., Tophinke, D., &; Hüllermeier, E. (2017). Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German. In Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (pp. 40–45). Stroudsburg, PA, USA: Association for Computational Linguistics (ACL).https://doi.org/10.18653/v1/W17-2206 

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

Gupta, P., Hetzer, A., Tornede, T., Gottschalk, S., Kornelsen, A., Osterbrink, S.,  Hüllermeier, E. (2017). jPL: A Java-based Software Framework for Preference Learning. Presented at the WDA 2017 Workshops: KDML, FGWM, IR, and FGDB, Rostock.

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

Czech, M., Hüllermeier, E., Jakobs, M.-C., &; Wehrheim, H. (2017). Predicting Rankings of Software Verification Tools. In Proceedings of the 3rd International Workshop on Software Analytics (pp. 23–26).https://doi.org/10.1145/3121257.3121262 

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Fürnkranz, J., &; Hüllermeier, E. (2017). Preference Learning. In C. Sammut &; G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining (Vol. 107, pp. 1000–1005). Springer.

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

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

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

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

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