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440 Publications
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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2019 | Conference Paper | LibreCat-ID: 15011 |
A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking,” in Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, Dortmund, 2019, pp. 135–146.
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2019 | Conference Paper | LibreCat-ID: 15013
K. Brinker and E. Hüllermeier, “A Reduction of Label Ranking to Multiclass Classification,” in Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, 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 Abstract | LibreCat-ID: 13132
F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “From Automated to On-The-Fly Machine Learning,” in INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, Kassel, 2019, pp. 273–274.
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2019 | Conference Paper | LibreCat-ID: 10232 |
M. D. Wever, F. Mohr, A. Tornede, and E. Hüllermeier, “Automating Multi-Label Classification Extending ML-Plan,” presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA, 2019.
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2019 | Journal Article | LibreCat-ID: 20243
K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, and E. Hüllermeier, “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches,” IEEE Transactions on Cognitive and Developmental Systems, 2019, doi: 10.1109/TCDS.2019.2892991.
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2018 | Conference Paper | LibreCat-ID: 2479 |
F. Mohr, M. D. Wever, E. Hüllermeier, and A. Faez, “(WIP) Towards the Automated Composition of Machine Learning Services,” in SCC, San Francisco, CA, USA, 2018.
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2018 | Conference Paper | LibreCat-ID: 2857 |
F. Mohr, T. Lettmann, E. Hüllermeier, and M. D. Wever, “Programmatic Task Network Planning,” in Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, Delft, Netherlands, 2018, pp. 31–39.
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2018 | Conference Paper | LibreCat-ID: 2471 |
F. Mohr, M. D. Wever, and E. Hüllermeier, “On-The-Fly Service Construction with Prototypes,” in SCC, San Francisco, CA, USA, 2018.
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2018 | Journal Article | LibreCat-ID: 3402
V. Melnikov and E. Hüllermeier, “On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis,” Machine Learning, 2018.
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2018 | Journal Article | LibreCat-ID: 3510 |
F. Mohr, M. D. Wever, and E. Hüllermeier, “ML-Plan: Automated Machine Learning via Hierarchical Planning,” Machine Learning, pp. 1495–1515, 2018, doi: 10.1007/s10994-018-5735-z.
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2018 | Conference Paper | LibreCat-ID: 3552 |
F. Mohr, M. D. Wever, and E. Hüllermeier, “Reduction Stumps for Multi-Class Classification,” in Proceedings of the Symposium on Intelligent Data Analysis, ‘s-Hertogenbosch, the Netherlands.
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2018 | Conference Paper | LibreCat-ID: 3852 |
M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine Learning Pipelines,” in ICML 2018 AutoML Workshop, Stockholm, Sweden, 2018.
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2018 | Conference Paper | LibreCat-ID: 2109 |
M. D. Wever, F. Mohr, and E. Hüllermeier, “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, 2018.
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2018 | Preprint | LibreCat-ID: 17713 |
M. D. Wever, F. Mohr, and E. Hüllermeier, “Automated Multi-Label Classification based on ML-Plan.” Arxiv, 2018.
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2018 | Preprint | LibreCat-ID: 17714 |
F. Mohr, M. D. Wever, and E. Hüllermeier, “Automated machine learning service composition.” 2018.
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2018 | Book Chapter | LibreCat-ID: 6423
D. Schäfer and E. Hüllermeier, “Preference-Based Reinforcement Learning Using Dyad Ranking,” in Discovery Science, Cham: Springer International Publishing, 2018, pp. 161–175.
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