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418 Publications


2019 | Journal Article | LibreCat-ID: 10578
Tagne VK, Fotso S, Fono LA, Hüllermeier E. Choice Functions Generated by Mallows and Plackett–Luce Relations. New Mathematics and Natural Computation. 2019;15(2):191-213.
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2019 | Book Chapter | LibreCat-ID: 15004
Ahmadi Fahandar M, Hüllermeier E. Feature Selection for Analogy-Based Learning to Rank. In: Discovery Science. Cham; 2019. doi:10.1007/978-3-030-33778-0_22
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2019 | Conference Paper | LibreCat-ID: 15009
Epple N, Dari S, Drees L, Protschky V, Riener A. Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries. In: 2019 IEEE Intelligent Vehicles Symposium (IV). ; 2019. doi:10.1109/ivs.2019.8814100
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2019 | Conference Paper | LibreCat-ID: 15011
Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In: Hoffmann F, Hüllermeier E, Mikut R, eds. Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019. KIT Scientific Publishing, Karlsruhe; 2019:135-146.
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2019 | Book Chapter | LibreCat-ID: 15005
Ahmadi Fahandar M, Hüllermeier E. Analogy-Based Preference Learning with Kernels. In: KI 2019: Advances in Artificial Intelligence. Cham; 2019. doi:10.1007/978-3-030-30179-8_3
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2019 | Preprint | LibreCat-ID: 18016
Bengs V, Hüllermeier E. Preselection Bandits under the Plackett-Luce Model. arXiv:190706123.
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2019 | Conference Abstract | LibreCat-ID: 13132
Mohr F, Wever MD, Tornede A, Hüllermeier E. From Automated to On-The-Fly Machine Learning. In: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik. Bonn: Gesellschaft für Informatik e.V.; 2019:273-274.
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2019 | Journal Article | LibreCat-ID: 14027
Bengs V, Eulert M, Holzmann H. Asymptotic confidence sets for the jump curve in bivariate regression problems. Journal of Multivariate Analysis. 2019:291-312. doi:10.1016/j.jmva.2019.02.017
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2019 | Journal Article | LibreCat-ID: 15001
Couso I, Borgelt C, Hüllermeier E, Kruse R. Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Computational Intelligence Magazine. 2019:31-44. doi:10.1109/mci.2018.2881642
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2019 | Book Chapter | LibreCat-ID: 15006
Nguyen V-L, Destercke S, Hüllermeier E. Epistemic Uncertainty Sampling. In: Discovery Science. Cham; 2019. doi:10.1007/978-3-030-33778-0_7
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2019 | Conference Paper | LibreCat-ID: 15013
Brinker K, Hüllermeier E. A Reduction of Label Ranking to Multiclass Classification. In: Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany; 2019.
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2019 | Journal Article | LibreCat-ID: 15025
Wever MD, van Rooijen L, Hamann H. Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary Computation. doi:10.1162/evco_a_00266
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2019 | Conference Abstract | LibreCat-ID: 8868
Wever MD, Mohr F, Hüllermeier E, Hetzer A. Towards Automated Machine Learning for Multi-Label Classification. In: ; 2019.
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2018 | Conference Paper | LibreCat-ID: 10184
Schäfer D, Hüllermeier E. Preference-Based Reinforcement Learning Using Dyad Ranking. In: Proc. 21st Int. Conference on Discovery Science (DS). ; 2018:161-175.
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2018 | Conference Paper | LibreCat-ID: 2479
Mohr F, Wever MD, Hüllermeier E, Faez A. (WIP) Towards the Automated Composition of Machine Learning Services. In: SCC. San Francisco, CA, USA: IEEE; 2018. doi:10.1109/SCC.2018.00039
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2018 | Conference Paper | LibreCat-ID: 3852
Wever MD, Mohr F, Hüllermeier E. ML-Plan for Unlimited-Length Machine Learning Pipelines. In: ICML 2018 AutoML Workshop. ; 2018.
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2018 | Conference (Editor) | LibreCat-ID: 10591
Abiteboul S, Arenas M, Barceló P, et al., eds. Research Directions for Principles of Data Management. Vol 7.; 2018:1-29.
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2018 | Bachelorsthesis | LibreCat-ID: 5936
Scheibl M. Learning about Learning Curves from Dataset Properties.; 2018.
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2018 | Preprint | LibreCat-ID: 17713
Wever MD, Mohr F, Hüllermeier E. Automated Multi-Label Classification based on ML-Plan. 2018.
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2018 | Preprint | LibreCat-ID: 19524
Pfannschmidt K, Gupta P, Hüllermeier E. Deep Architectures for Learning Context-dependent Ranking Functions. arXiv:180305796. 2018.
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2018 | Conference Paper | LibreCat-ID: 10148
El Mesaoudi-Paul A, Hüllermeier E, Busa-Fekete R. Ranking Distributions based on  Noisy Sorting. In: Proc. 35th Int. Conference on Machine Learning (ICML). ; 2018:3469-3477.
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2018 | Conference Paper | LibreCat-ID: 10181
Nguyen V-L, Destercke S, Masson M-H, Hüllermeier E. Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty. In: Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI). ; 2018:5089-5095.
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2018 | Journal Article | LibreCat-ID: 16038
Schäfer D, Hüllermeier E. Dyad ranking using Plackett-Luce models based on joint feature representations. Machine Learning. 2018;107(5):903-941.
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2018 | Conference Paper | LibreCat-ID: 2109
Wever MD, Mohr F, Hüllermeier E. 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; 2018. doi:10.1145/3205455.3205562
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2018 | Conference Paper | LibreCat-ID: 2471
Mohr F, Wever MD, Hüllermeier E. On-The-Fly Service Construction with Prototypes. In: SCC. San Francisco, CA, USA: IEEE Computer Society; 2018. doi:10.1109/SCC.2018.00036
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2018 | Book Chapter | LibreCat-ID: 6423
Schäfer D, Hüllermeier E. Preference-Based Reinforcement Learning Using Dyad Ranking. In: Discovery Science. Cham: Springer International Publishing; 2018:161-175. doi:10.1007/978-3-030-01771-2_11
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2018 | Preprint | LibreCat-ID: 17714
Mohr F, Wever MD, Hüllermeier E. Automated machine learning service composition. 2018.
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2018 | Journal Article | LibreCat-ID: 10276
Schäfer D, Hüllermeier E. Dyad Ranking Using Plackett-Luce Models based on joint feature representations. Machine Learning. 2018;107(5):903-941.
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2018 | Conference Paper | LibreCat-ID: 10149
Hesse M, Timmermann J, Hüllermeier E, Trächtler A. A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. In: Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected Systems in Products and Production, Procedia Manufacturing 24. ; 2018:15-20.
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2018 | Book Chapter | LibreCat-ID: 10783
Couso I, Hüllermeier E. Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In: Mostaghim S, Nürnberger A, Borgelt C, eds. Frontiers in Computational Intelligence. Springer; 2018:31-46.
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2018 | Conference Paper | LibreCat-ID: 2857
Mohr F, Lettmann T, Hüllermeier E, Wever MD. Programmatic Task Network Planning. In: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning. AAAI; 2018:31-39.
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2018 | Conference Paper | LibreCat-ID: 3552
Mohr F, Wever MD, Hüllermeier E. Reduction Stumps for Multi-Class Classification. In: Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands. doi:10.1007/978-3-030-01768-2_19
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2018 | Conference Paper | LibreCat-ID: 10145
Ahmadi Fahandar M, Hüllermeier E. Learning to Rank Based on Analogical Reasoning. In: Proc. 32 Nd AAAI Conference on Artificial Intelligence (AAAI). ; 2018:2951-2958.
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2018 | Book Chapter | LibreCat-ID: 10152
Mencia EL, Fürnkranz J, Hüllermeier E, Rapp M. Learning interpretable rules for multi-label classification. In: Jair Escalante H, Escalera S, Guyon I, et al., eds. Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer; 2018:81-113.
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2018 | Conference Abstract | LibreCat-ID: 1379
Seemann N, Geierhos M, Merten M-L, Tophinke D, Wever MD, Hüllermeier E. Supporting the Cognitive Process in Annotation Tasks. In: Eckart K, Schlechtweg D, eds. Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft. ; 2018.
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2018 | Journal Article | LibreCat-ID: 3402
Melnikov V, Hüllermeier E. On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning. 2018. doi:10.1007/s10994-018-5733-1
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2018 | Journal Article | LibreCat-ID: 3510
Mohr F, Wever MD, Hüllermeier E. ML-Plan: Automated Machine Learning via Hierarchical Planning. Machine Learning. 2018:1495-1515. doi:10.1007/s10994-018-5735-z
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2018 | Bachelorsthesis | LibreCat-ID: 5693
Graf H. Ranking of Classification Algorithms in AutoML.; 2018.
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2017 | Conference Paper | LibreCat-ID: 10204
Ewerth R, Springstein M, Müller E, et al. Estimating relative depth in single images via rankboost. In: Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017). ; 2017:919-924.
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2017 | Conference Paper | LibreCat-ID: 10209
Ahmadi Fahandar M, Hüllermeier E. Learning to Rank based on Analogical Reasoning. In: Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence. ; 2017.
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2017 | Conference Paper | LibreCat-ID: 10216
Shaker A, Heldt W, Hüllermeier E. Learning TSK Fuzzy Rules from Data Streams. In: Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Skopje, Macedonia. ; 2017.
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2017 | Conference Paper | LibreCat-ID: 1180
Wever MD, Mohr F, Hüllermeier E. Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. In: 27th Workshop Computational Intelligence. Dortmund; 2017.
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2017 | Conference Paper | LibreCat-ID: 15110
Couso I, Dubois D, Hüllermeier E. Maximum likelihood estimation and coarse data. In: In Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain. Springer; 2017:3-16.
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2017 | Bachelorsthesis | LibreCat-ID: 5694
Schnitker NN. Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies.; 2017.
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2017 | Encyclopedia Article | LibreCat-ID: 10589
Fürnkranz J, Hüllermeier E. Preference Learning. In: Encyclopedia of Machine Learning and Data Mining. ; 2017:1000-1005.
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2017 | Conference Paper | LibreCat-ID: 10205
Ahmadi Fahandar M, Hüllermeier E, Couso I. Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening. In: Proc. 34th Int. Conf. on Machine Learning (ICML 2017). ; 2017:1078-1087.
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2017 | Conference Paper | LibreCat-ID: 10212
Hoffmann F, Hüllermeier E, Mikut R. (Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017. In: ; 2017.
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2017 | Journal Article | LibreCat-ID: 10267
Bräuning M, Hüllermeier E, Keller T, Glaum M. Lexicographic preferences for predictive modeling of human decision making. A new machine learning method with an application  in accounting. European Journal of Operational Research. 2017;258(1):295-306.
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2017 | Conference Paper | LibreCat-ID: 15399
Czech M, Hüllermeier E, Jacobs MC, Wehrheim H. 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. ; 2017.
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2017 | Conference Abstract | LibreCat-ID: 5722
Gupta P, Hetzer A, Tornede T, et al. jPL: A Java-based Software Framework for Preference Learning. In: ; 2017.
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