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409 Publications


2020 | Conference Paper | LibreCat-ID: 17407
A. Tornede, M. D. Wever, and E. Hüllermeier, “Extreme Algorithm Selection with Dyadic Feature Representation,” in Discovery Science, 2020.
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2020 | Conference Paper | LibreCat-ID: 17408
J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Hybrid Ranking and Regression for Algorithm Selection,” in KI 2020: Advances in Artificial Intelligence, 2020.
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2020 | Preprint | LibreCat-ID: 17605
S. H. Heid, M. D. Wever, and E. Hüllermeier, “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction,” Journal of Data Mining and Digital Humanities. episciences.
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2020 | Conference Paper | LibreCat-ID: 15629
M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification,” presented at the Symposium on Intelligent Data Analysis, Konstanz, Germany.
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2020 | Conference Paper | LibreCat-ID: 17424
T. Tornede, A. Tornede, M. D. Wever, F. Mohr, and E. Hüllermeier, “AutoML for Predictive Maintenance: One Tool to RUL them all,” in Proceedings of the ECMLPKDD 2020, 2020.
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2020 | Journal Article | LibreCat-ID: 16725
C. Richter, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Algorithm Selection for Software Validation Based on Graph Kernels,” Journal of Automated Software Engineering.
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2019 | Journal Article | LibreCat-ID: 14028
V. Bengs and H. Holzmann, “Adaptive confidence sets for kink estimation,” Electronic Journal of Statistics, pp. 1523–1579, 2019.
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2019 | Journal Article | LibreCat-ID: 15002
W. Waegeman, K. Dembczynski, and E. Hüllermeier, “Multi-target prediction: a unifying view on problems and methods,” Data Mining and Knowledge Discovery, vol. 33, no. 2, pp. 293–324, 2019.
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2019 | Conference Paper | LibreCat-ID: 15014
E. Hüllermeier, I. Couso, and S. Diestercke, “Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants,” in Proceedings SUM 2019, International Conference on Scalable Uncertainty Management, 2019.
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2019 | Conference Paper | LibreCat-ID: 15007
V. Melnikov and E. Hüllermeier, “Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA,” in Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019.
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