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 

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

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 

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

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 

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

Fürnkranz, J., &; Hüllermeier, E. (2017). Preference Learning. In Encyclopedia of Machine Learning and Data Mining (pp. 1000–1005).

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.

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

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

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).

Mohr, F., Lettmann, T., &; Hüllermeier, E. (2017). Planning with Independent Task Networks. In Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017) (pp. 193–206).https://doi.org/10.1007/978-3-319-67190-1_15 

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

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

Ahmadi Fahandar, M., &; Hüllermeier, E. (2017). Learning to Rank based on Analogical Reasoning. In Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence.

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

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.

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

Melnikov, V., &; Hüllermeier, E. (2016). 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 (pp. 756–771).

Dembczynski, K., Kotlowski, W., Waegeman, W., Busa-Fekete, R., &; Hüllermeier, E. (2016). 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 (pp. 511–526).

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

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.

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.), Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.

Schäfer, D., &; Hüllermeier, E. (2016). Preference-Based Reinforcement Learning Using Dyad Ranking. 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.

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.), Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.

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

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

Kaminka, G. A., Fox, M., Bouquet, P., Hüllermeier, E., Dignum, V., Dignum, F., &; van Harmelen, F. (Eds.). (2016). ECAI 2016, 22nd European Conference on Artificial Intelligence, including PAIS 2016, Prestigious Applications of Artificial Intelligence (Vol. 285). The Hague, The Netherlands: IOS Press.

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.

Riemenschneider, M., Senge, R., Neumann, U., Hüllermeier, E., &; Heider, D. (2016). Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification. BioData Mining, 9(10).

Arifulina, S., Platenius, M. C., Mohr, F., Engels, G., &; Schäfer, W. (2015). Market-Specific Service Compositions: Specification and Matching. In Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet (pp. 333--340).https://doi.org/10.1109/SERVICES.2015.58 

Jungmann, A., &; Mohr, F. (2015). An approach towards adaptive service composition in markets of composed services. Journal of Internet Services and Applications, (1), 1–18.https://doi.org/10.1186/s13174-015-0022-8 

Mohr, F. (2015). A Metric for Functional Reusability of Services. In Proceedings of the 14th International Conference on Software Reuse (ICSR) (pp. 298--313).https://doi.org/10.1007/978-3-319-14130-5_21 

Mohr, F., Jungmann, A., &; Kleine Büning, H. (2015). Automated Online Service Composition. In Proceedings of the 12th IEEE International Conference on Services Computing (SCC) (pp. 57--64).https://doi.org/10.1109/SCC.2015.18 

Senge, R., &; Hüllermeier, E. (2015). Fast Fuzzy Pattern Tree Learning for Classification. IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033.https://doi.org/10.1109/tfuzz.2015.2396078 

Schäfer, D., &; Hüllermeier, E. (2015). Preference-based meta-learning using dyad ranking: Recommending algorithms in cold-start situations. In in Proceedings of the 2015 international Workshop on Meta-Learning and Algorithm Selection co-located ECML/PKDD, Porto, Portugal (pp. 110–111).

Paul, A., &; Hüllermeier, E. (2015). A cbr approach to the angry birds game. In In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany (pp. 68–77).

Ewerth, R., Balz, A., Gehlhaar, J., Dembczynski, K., &; Hüllermeier, E. (2015). Depth estimation in monocular images: Quantitative versus qualitative approaches. In F. Hoffmann &; E. Hüllermeier (Eds.), In Proceedings 25. Workshop Computational Intelligence, Dortmund, Germany (pp. 235–240). KIT Scientific Publishing.

Lu, S., &; Hüllermeier, E. (2015). Locally weighted regression through data imprecisiation. In F. Hoffmann &; E. Hüllermeier (Eds.), in Proceedings 25th Workshop Computational Intelligence, Dortmund Germany (pp. 97–104). KIT Scientific Publishing.

Senge, R., &; Hüllermeier, E. (2015). Fast fuzzy pattern tree learning for classification . IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033.

Hüllermeier, E. (2015). From knowledge-based to data driven fuzzy modeling: Development, criticism and alternative directions. Informatik Spektrum, 38(6), 500–509.

Hüllermeier, E. (2015). Does machine learning need fuzzy logic? Fuzzy Sets and Systems, 281, 292–299.

Waegeman, W., Dembczynski, K., Jachnik, A., Cheng, W., &; Hüllermeier, E. (2015). On the Bayes-optimality of F-measure maximizers. Journal of Machine Learning Research, 15, 3313–3368.

Shaker, A., &; Hüllermeier, E. (2015). Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing, 150, 250–264.

Hüllermeier, E., &; Minor, M. (2015). Case-Based Reasoning Research and Development . In in Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) LNAI 9343. Springer.

Hoffmann, F., &; Hüllermeier, E. (2015). Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing.

Abdel-Aziz, A., &; Hüllermeier, E. (2015). Case Base Maintenance in Preference-Based CBR. In In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) (pp. 1–14).

Szörényi, B., Busa-Fekete, R., Weng, P., &; Hüllermeier, E. (2015). Qualitative Multi-Armed Bandits: A Quantile-Based Approach. In In Proceedings International Conference on Machine Learning (ICML 2015) (pp. 1660–1668).

Schäfer, D., &; Hüllermeier, E. (2015). Dyad Ranking Using A Bilinear Plackett-Luce Model. In in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (pp. 227–242).

Hüllermeier, E., &; Cheng, W. (2015). Superset Learning Based on Generalized Loss Minimization . In in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (pp. 260–275).

Henzgen, S., &; Hüllermeier, E. (2015). Weighted Rank Correlationf: A Flexible Approach Based on Fuzzy Order Relations. In in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (pp. 422–437).

Szörényi, B., Busa-Fekete, R., Paul, A., &; Hüllermeier, E. (2015). Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach. In in Advances in Neural Information Processing Systems 28 (NIPS 2015) (pp. 604–612).

Szörényi, B., Busa-Fekete, R., Dembczynski, K., &; Hüllermeier, E. (2015). Online F-Measure Optimization. In in Advances in Neural Information Processing Systems 28 (NIPS 2015) (pp. 595–603).

El Mesaoudi-Paul, A., &; Hüllermeier, E. (2015). A CBR Approach to the Angry Birds Game. In in Workshop Proc. 23rd International Conference on Case-Based Reasoning (ICCBR 2015) (pp. 68–77).

Schäfer, D., &; Hüllermeier, E. (2015). Preference-Based Meta- Learning Using Dyad Ranking: Recommending Algorithms in Cold-Start Situations. In in Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection (MetaSel@PKDD/ECML) (pp. 110–111).

Lu, S., &; Hüllermeier, E. (2015). Locally weighted regression through data imprecisiation. In Proceedings 25. Workshop Computational Intelligence (pp. 97–104).

Ewerth, R., Balz, A., Gehlhaar, J., Dembczynski, K., &; Hüllermeier, E. (2015). Depth estimation in monocular images: Quantitative versus qualitative approaches. In Proceedings 25. Workshop Computational Intelligence (pp. 235–240).