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434 Publications


2020 | Preprint | LibreCat-ID: 21536
Bengs, Viktor, and Eyke Hüllermeier. “Multi-Armed Bandits with Censored Consumption of Resources.” ArXiv:2011.00813, 2020.
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2020 | Conference Paper | LibreCat-ID: 15629
Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification.” Springer, n.d.
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2020 | Conference Paper | LibreCat-ID: 20306
Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Towards Meta-Algorithm Selection.” In Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.
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2020 | Conference Paper | LibreCat-ID: 17424
Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, Felix Mohr, and Eyke Hüllermeier. “AutoML for Predictive Maintenance: One Tool to RUL Them All.” In Proceedings of the ECMLPKDD 2020, 2020. https://doi.org/10.1007/978-3-030-66770-2_8.
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2020 | Book Chapter | LibreCat-ID: 19521
Pfannschmidt, Karlson, and Eyke Hüllermeier. “Learning Choice Functions via Pareto-Embeddings.” In Lecture Notes in Computer Science. Cham, 2020. https://doi.org/10.1007/978-3-030-58285-2_30.
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2020 | Journal Article | LibreCat-ID: 16725
Richter, Cedric, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim. “Algorithm Selection for Software Validation Based on Graph Kernels.” Journal of Automated Software Engineering, n.d.
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2020 | Conference Paper | LibreCat-ID: 18276
Tornede, Alexander, Marcel Dominik Wever, Stefan Werner, Felix Mohr, and Eyke Hüllermeier. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” In ACML 2020, 2020.
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2020 | Preprint | LibreCat-ID: 18017
El Mesaoudi-Paul, Adil, Viktor Bengs, and Eyke Hüllermeier. “Online Preselection with Context Information under the Plackett-Luce  Model.” ArXiv:2002.04275, n.d.
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2020 | Conference Paper | LibreCat-ID: 21534
Bengs, Viktor, and Eyke Hüllermeier. “Preselection Bandits.” In International Conference on Machine Learning, 778–87, 2020.
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2019 | Journal Article | LibreCat-ID: 14028
Bengs, Viktor, and Hajo Holzmann. “Adaptive Confidence Sets for Kink Estimation.” Electronic Journal of Statistics, 2019, 1523–79. https://doi.org/10.1214/19-ejs1555.
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