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409 Publications


2020 | Conference Paper | LibreCat-ID: 17407
Tornede A, Wever MD, Hüllermeier E. Extreme Algorithm Selection with Dyadic Feature Representation. In: Discovery Science. ; 2020.
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2020 | Conference Paper | LibreCat-ID: 17408
Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Hybrid Ranking and Regression for Algorithm Selection. In: KI 2020: Advances in Artificial Intelligence. ; 2020.
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2020 | Preprint | LibreCat-ID: 17605
Heid SH, Wever MD, Hüllermeier E. Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. Journal of Data Mining and Digital Humanities.
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2020 | Conference Paper | LibreCat-ID: 15629
Wever MD, Tornede A, Mohr F, Hüllermeier E. LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. In: Springer.
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2020 | Conference Paper | LibreCat-ID: 17424
Tornede T, Tornede A, Wever MD, Mohr F, Hüllermeier E. 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
Richter C, Hüllermeier E, Jakobs M-C, Wehrheim H. Algorithm Selection for Software Validation Based on Graph Kernels. Journal of Automated Software Engineering.
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2019 | Journal Article | LibreCat-ID: 14028
Bengs V, Holzmann H. Adaptive confidence sets for kink estimation. Electronic Journal of Statistics. 2019:1523-1579. doi:10.1214/19-ejs1555
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2019 | Journal Article | LibreCat-ID: 15002
Waegeman W, Dembczynski K, Hüllermeier E. Multi-target prediction: a unifying view on problems and methods. Data Mining and Knowledge Discovery. 2019;33(2):293-324. doi:10.1007/s10618-018-0595-5
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2019 | Conference Paper | LibreCat-ID: 15014
Hüllermeier E, Couso I, Diestercke S. 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
Melnikov V, Hüllermeier E. 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. doi:10.1016/j.jmva.2019.02.017
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