Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak Convergence Theory.” ArXiv:1903.09864, 2019.

Wever, Marcel Dominik, et al. Towards Automated Machine Learning for Multi-Label Classification. 2019.

Tagne, V. K., et al. “Choice Functions Generated by Mallows and Plackett–Luce Relations.” New Mathematics and Natural Computation, vol. 15, no. 2, 2019, pp. 191–213.

Couso, Ines, et al. “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning.”10.1109/mci.2018.2881642__IEEE Computational Intelligence Magazine, 2019, pp. 31–44, doi:10.1109/mci.2018.2881642.

Waegeman, Willem, et al. “Multi-Target Prediction: A Unifying View on Problems and Methods.”10.1007/s10618-018-0595-5__Data Mining and Knowledge Discovery, vol. 33, no. 2, 2019, pp. 293–324, doi:10.1007/s10618-018-0595-5.

Mortier, Thomas, et al. “Set-Valued Prediction in Multi-Class Classification.” Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019, 2019.

Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Feature Selection for Analogy-Based Learning to Rank.”10.1007/978-3-030-33778-0_22__Discovery Science, 2019, doi:10.1007/978-3-030-33778-0_22.

Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Analogy-Based Preference Learning with Kernels.”10.1007/978-3-030-30179-8_3__KI 2019: Advances in Artificial Intelligence, 2019, doi:10.1007/978-3-030-30179-8_3.

Nguyen, Vu-Linh, et al. “Epistemic Uncertainty Sampling.”10.1007/978-3-030-33778-0_7__Discovery Science, 2019, doi:10.1007/978-3-030-33778-0_7.

Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA.”10.1016/j.jmva.2019.02.017__Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019, doi:10.1016/j.jmva.2019.02.017.

Epple, Nico, et al. “Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries.”10.1109/ivs.2019.8814100__2019 IEEE Intelligent Vehicles Symposium (IV), 2019, doi:10.1109/ivs.2019.8814100.

Tornede, Alexander, et al. “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking.” Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, edited by Frank Hoffmann et al., KIT Scientific Publishing, Karlsruhe, 2019, pp. 135–46.

Brinker, Klaus, and Eyke Hüllermeier. “A Reduction of Label Ranking to Multiclass Classification.” Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, 2019.

Hüllermeier, Eyke, et al. “Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants.” Proceedings SUM 2019, International Conference on Scalable Uncertainty Management, 2019.

Henzgen, Sascha, and Eyke Hüllermeier. “Mining Rank Data.”10.1145/3363572__ACM Transactions on Knowledge Discovery from Data, 2019, pp. 1–36, doi:10.1145/3363572.

Bengs, Viktor, et al. “Asymptotic Confidence Sets for the Jump Curve in Bivariate Regression Problems.”10.1016/j.jmva.2019.02.017__Journal of Multivariate Analysis, 2019, pp. 291–312, doi:10.1016/j.jmva.2019.02.017.

Bengs, Viktor, and Hajo Holzmann. “Adaptive Confidence Sets for Kink Estimation.”10.1214/19-ejs1555__Electronic Journal of Statistics, 2019, pp. 1523–79, doi:10.1214/19-ejs1555.

Mohr, Felix, et al. “From Automated to On-The-Fly Machine Learning.” INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, Gesellschaft für Informatik e.V., 2019, pp. 273–74.

Wever, Marcel Dominik, et al. Automating Multi-Label Classification Extending ML-Plan. 2019.

Rohlfing, Katharina, et al. “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches.”10.1109/TCDS.2019.2892991__IEEE Transactions on Cognitive and Developmental Systems, 2019, doi:10.1109/TCDS.2019.2892991.