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409 Publications


2019 | Journal Article | LibreCat-ID: 14027
Bengs, V., Eulert, M., & Holzmann, H. (2019). Asymptotic confidence sets for the jump curve in bivariate regression problems. Journal of Multivariate Analysis, 291–312. https://doi.org/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. (2019). Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Computational Intelligence Magazine, 31–44. https://doi.org/10.1109/mci.2018.2881642
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2019 | Book Chapter | LibreCat-ID: 15006
Nguyen, V.-L., Destercke, S., & Hüllermeier, E. (2019). Epistemic Uncertainty Sampling. In Discovery Science. Cham. https://doi.org/10.1007/978-3-030-33778-0_7
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2019 | Conference Paper | LibreCat-ID: 15013
Brinker, K., & Hüllermeier, E. (2019). A Reduction of Label Ranking to Multiclass Classification. In Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany.
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2019 | Journal Article | LibreCat-ID: 15025
Wever, M. D., van Rooijen, L., & Hamann, H. (n.d.). Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary Computation. https://doi.org/10.1162/evco_a_00266
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2019 | Conference Abstract | LibreCat-ID: 8868
Wever, M. D., Mohr, F., Hüllermeier, E., & Hetzer, A. (2019). Towards Automated Machine Learning for Multi-Label Classification. Presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany.
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2018 | Conference Paper | LibreCat-ID: 10184
Schäfer, D., & Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. In Proc. 21st Int. Conference on Discovery Science (DS) (pp. 161–175).
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2018 | Conference Paper | LibreCat-ID: 2479
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
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2018 | Conference Paper | LibreCat-ID: 3852
Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). ML-Plan for Unlimited-Length Machine Learning Pipelines. In ICML 2018 AutoML Workshop. Stockholm, Sweden.
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2018 | Conference (Editor) | LibreCat-ID: 10591
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).
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2018 | Bachelorsthesis | LibreCat-ID: 5936
Scheibl, M. (2018). Learning about learning curves from dataset properties.
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2018 | Preprint | LibreCat-ID: 17713
Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). Automated Multi-Label Classification based on ML-Plan. Arxiv.
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2018 | Conference Paper | LibreCat-ID: 10148
El Mesaoudi-Paul, A., Hüllermeier, E., & Busa-Fekete, R. (2018). Ranking Distributions based on  Noisy Sorting. In Proc. 35th Int. Conference on Machine Learning (ICML) (pp. 3469–3477).
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2018 | Conference Paper | LibreCat-ID: 10181
Nguyen, V.-L., Destercke, S., Masson, M.-H., & Hüllermeier, E. (2018). Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty. In Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI) (pp. 5089–5095).
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2018 | Journal Article | LibreCat-ID: 16038
Schäfer, D., & Hüllermeier, E. (2018). Dyad ranking using Plackett-Luce models based on joint feature representations. Machine Learning, 107(5), 903–941.
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2018 | Conference Paper | LibreCat-ID: 2109
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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2018 | Conference Paper | LibreCat-ID: 2471
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
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2018 | Book Chapter | LibreCat-ID: 6423
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
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2018 | Preprint | LibreCat-ID: 17714
Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). Automated machine learning service composition.
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2018 | Journal Article | LibreCat-ID: 10276
Schäfer, D., & Hüllermeier, E. (2018). Dyad Ranking Using Plackett-Luce Models based on joint feature representations. Machine Learning, 107(5), 903–941.
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