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434 Publications


2019 | Journal Article | LibreCat-ID: 15025
Wever, Marcel Dominik, Lorijn van Rooijen, and Heiko Hamann. “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.” Evolutionary Computation, n.d. https://doi.org/10.1162/evco_a_00266.
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2018 | Conference Paper | LibreCat-ID: 10184
Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning Using Dyad Ranking.” In Proc. 21st Int. Conference on Discovery Science (DS), 161–75, 2018.
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2018 | Conference Paper | LibreCat-ID: 2479
Mohr, Felix, Marcel Dominik Wever, Eyke Hüllermeier, and Amin Faez. “(WIP) Towards the Automated Composition of Machine Learning Services.” In SCC. San Francisco, CA, USA: IEEE, 2018. https://doi.org/10.1109/SCC.2018.00039.
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2018 | Conference Paper | LibreCat-ID: 3852
Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “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., M. Arenas, P. Barceló, M. Bienvenu, D. Calvanese, C. David, R. Hull, et al., eds. Research Directions for Principles of Data Management. Vol. 7, 2018.
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2018 | Preprint | LibreCat-ID: 17713
Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automated Multi-Label Classification Based on ML-Plan.” Arxiv, 2018.
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2018 | Bachelorsthesis | LibreCat-ID: 5936
Scheibl, Manuel. Learning about Learning Curves from Dataset Properties. Universität Paderborn, 2018.
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2018 | Preprint | LibreCat-ID: 19524
Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Deep Architectures for Learning Context-Dependent Ranking Functions.” ArXiv:1803.05796, 2018.
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2018 | Conference Paper | LibreCat-ID: 10148
El Mesaoudi-Paul, Adil, Eyke Hüllermeier, and Robert Busa-Fekete. “Ranking Distributions Based on  Noisy Sorting.” In Proc. 35th Int. Conference on Machine Learning (ICML), 3469–77, 2018.
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2018 | Conference Paper | LibreCat-ID: 10181
Nguyen, Vu-Linh, Sebastian Destercke, M.-H. Masson, and Eyke Hüllermeier. “Reliable Multi-Class Classification Based on Pairwise Epistemic and Aleatoric Uncertainty.” In Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 5089–95, 2018.
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