Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph Neural Networks.”10.1007/978-3-030-88942-5__Proceedings of The 24th International Conference on Discovery Science (DS 2021), edited by Carlos Soares and Luis Torgo, vol. 12986, Springer, 2021, pp. 166–80, doi:10.1007/978-3-030-88942-5.

Wever, Marcel Dominik. Automated Machine Learning for Multi-Label Classification. 2021, doi:10.17619/UNIPB/1-1302.

Hanselle, Jonas Manuel, et al. Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. 2021.

Pfannschmidt, Karlson, and Eyke Hüllermeier. “Learning Choice Functions via Pareto-Embeddings.”10.1007/978-3-030-58285-2_30__Lecture Notes in Computer Science, 2020, doi:10.1007/978-3-030-58285-2_30.

Damke, Clemens, et al. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.” Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), edited by Sinno Jialin Pan and Masashi Sugiyama, vol. 129, PMLR, 2020, pp. 49–64.

Bengs, Viktor, and Eyke Hüllermeier. “Preselection Bandits.” International Conference on Machine Learning, 2020, pp. 778–87.

Bengs, Viktor, and Eyke Hüllermeier. “Multi-Armed Bandits with Censored Consumption of Resources.” ArXiv:2011.00813, 2020.

Tornede, Alexander, et al. “Extreme Algorithm Selection with Dyadic Feature Representation.” Discovery Science, 2020.

Hanselle, Jonas Manuel, et al. “Hybrid Ranking and Regression for Algorithm Selection.” KI 2020: Advances in Artificial Intelligence, 2020.

Tornede, Tanja, et al. “AutoML for Predictive Maintenance: One Tool to RUL Them All.”10.1007/978-3-030-66770-2_8__Proceedings of the ECMLPKDD 2020, 2020, doi:10.1007/978-3-030-66770-2_8.

Heid, Stefan Helmut, et al. “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction.” Journal of Data Mining and Digital Humanities, episciences.

Tornede, Alexander, et al. “Towards Meta-Algorithm Selection.” Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.

El Mesaoudi-Paul, Adil, et al. “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach.”10.1007/978-3-030-53552-0_22__Learning and Intelligent Optimization. LION 2020., vol. 12096, Springer, 2020, pp. 216–32, doi:10.1007/978-3-030-53552-0_22.

El Mesaoudi-Paul, Adil, et al. “Online Preselection with Context Information under the Plackett-Luce  Model.” ArXiv:2002.04275.

Tornede, Alexander, et al. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” ACML 2020, 2020.

Richter, Cedric, et al. “Algorithm Selection for Software Validation Based on Graph Kernels.” Journal of Automated Software Engineering, Springer.

Wever, Marcel Dominik, et al. LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Springer.

Wever, Marcel Dominik, et al. “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.”10.1162/evco_a_00266__Evolutionary Computation, vol. 28, no. 2, MIT Press Journals, 2020, pp. 165–193, doi:10.1162/evco_a_00266.

Pfannschmidt, Karlson, et al. “Learning Choice Functions: Concepts and Architectures.” ArXiv:1901.10860, 2019.

Merten, Marie-Luis, et al. “Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff.” Niederdeutsches Jahrbuch, no. 142, 2019, pp. 124–46.