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449 Publications
2019 | Conference Paper | LibreCat-ID: 15009
Epple, N., Dari, S., Drees, L., Protschky, V., & Riener, A. (2019). Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries. In 2019 IEEE Intelligent Vehicles Symposium (IV). https://doi.org/10.1109/ivs.2019.8814100
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2019 | Conference Paper | LibreCat-ID: 15011 | 
Tornede, A., Wever, M. D., & Hüllermeier, E. (2019). Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019 (pp. 135–146). Dortmund: KIT Scientific Publishing, Karlsruhe.
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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 | Conference Paper | LibreCat-ID: 15014
Hüllermeier, E., Couso, I., & Diestercke, S. (2019). Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants. In Proceedings SUM 2019, International Conference on Scalable Uncertainty Management.
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2019 | Journal Article | LibreCat-ID: 15015
Henzgen, S., & Hüllermeier, E. (2019). Mining Rank Data. ACM Transactions on Knowledge Discovery from Data, 1–36. https://doi.org/10.1145/3363572
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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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| DOI
2019 | Journal Article | LibreCat-ID: 14028
Bengs, V., & Holzmann, H. (2019). Adaptive confidence sets for kink estimation. Electronic Journal of Statistics, 1523–1579. https://doi.org/10.1214/19-ejs1555
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2019 | Conference Abstract | LibreCat-ID: 13132
Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (2019). From Automated to On-The-Fly Machine Learning. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (pp. 273–274). Bonn: Gesellschaft für Informatik e.V.
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2019 | Conference Paper | LibreCat-ID: 10232 |
Wever, M. D., Mohr, F., Tornede, A., & Hüllermeier, E. (2019). Automating Multi-Label Classification Extending ML-Plan. Presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA.
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2019 | Journal Article | LibreCat-ID: 20243
Rohlfing, K., Leonardi, G., Nomikou, I., Rączaszek-Leonardi, J., & Hüllermeier, E. (2019). Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems. https://doi.org/10.1109/TCDS.2019.2892991
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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 | Preprint | LibreCat-ID: 19524
Pfannschmidt, K., Gupta, P., & Hüllermeier, E. (2018). Deep Architectures for Learning Context-dependent Ranking Functions. ArXiv:1803.05796.
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2018 | Conference Paper | LibreCat-ID: 2857 |
Mohr, F., Lettmann, T., Hüllermeier, E., & Wever, M. D. (2018). Programmatic Task Network Planning. In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (pp. 31–39). Delft, Netherlands: AAAI.
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2018 | Journal Article | LibreCat-ID: 24150
Ramaswamy, A., & Bhatnagar, S. (2018). Stability of stochastic approximations with “controlled markov” noise and temporal difference learning. IEEE Transactions on Automatic Control, 64(6), 2614–2620.
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2018 | Journal Article | LibreCat-ID: 24151
Demirel, B., Ramaswamy, A., Quevedo, D. E., & Karl, H. (2018). Deepcas: A deep reinforcement learning algorithm for control-aware scheduling. IEEE Control Systems Letters, 2(4), 737–742.
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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 | Journal Article | LibreCat-ID: 3402
Melnikov, V., & Hüllermeier, E. (2018). On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning. https://doi.org/10.1007/s10994-018-5733-1
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2018 | Journal Article | LibreCat-ID: 3510 |
Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). ML-Plan: Automated Machine Learning via Hierarchical Planning. Machine Learning, 1495–1515. https://doi.org/10.1007/s10994-018-5735-z
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2018 | Conference Paper | LibreCat-ID: 3552 |
Mohr, F., Wever, M. D., & Hüllermeier, E. (n.d.). Reduction Stumps for Multi-Class Classification. In Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands. https://doi.org/10.1007/978-3-030-01768-2_19
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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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