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434 Publications


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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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 | Preprint | LibreCat-ID: 17713
Wever MD, Mohr F, Hüllermeier E. Automated Multi-Label Classification based on ML-Plan. 2018.
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2018 | Bachelorsthesis | LibreCat-ID: 5936
Scheibl M. Learning about Learning Curves from Dataset Properties. Universität Paderborn; 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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