Tornede, Alexander. Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions, 2023.https://doi.org/10.17619/UNIPB/1-1780  .
LibreCat: /record/45780
DOI: 10.17619/UNIPB/1-1780 

Dellnitz, Michael, Eyke Hüllermeier, Marvin Lücke, Sina Ober-Blöbaum, Christian Offen, Sebastian Peitz, and Karlson Pfannschmidt. “Efficient Time Stepping for Numerical Integration Using Reinforcement  Learning.”https://doi.org/10.1137/21M1412682__SIAM Journal on Scientific Computing 45, no. 2 (2023): 9–95.https://doi.org/10.1137/21M1412682 .
LibreCat: /record/21600
DOI: 10.1137/21M1412682
arXiv: arXiv:2104.03562

Gevers, Karina, Volker Schöppner, and Eyke Hüllermeier. “Heated Tool Butt Welding of Two Different Materials –  Established Methods versus Artificial Intelligence,” 2021.
LibreCat: /record/24382

Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.”https://doi.org/10.1109/tpami.2021.3051276__IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 1–1.https://doi.org/10.1109/tpami.2021.3051276 .
LibreCat: /record/21004
DOI: 10.1109/tpami.2021.3051276

Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence, n.d.
LibreCat: /record/21092

Bengs, Viktor, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, and Eyke Hüllermeier. “Preference-Based Online Learning with Dueling Bandits: A Survey.” Journal of Machine Learning Research 22, no. 7 (2021): 1–108.
LibreCat: /record/21535

Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” In Proceedings of the Genetic and Evolutionary Computation Conference, 2021.
LibreCat: /record/21570

Bernijazov, Ruslan, Alexander Dicks, Roman Dumitrescu, Marc Foullois, Jonas Manuel Hanselle, Eyke Hüllermeier, Gökce Karakaya, et al. “A Meta-Review on Artiû01cial Intelligence in Product Creation.” In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21), 2021.
LibreCat: /record/23779

Hüllermeier, Eyke, Felix Mohr, Alexander Tornede, and Marcel Dominik Wever. “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” 2021.
LibreCat: /record/22913

Mohr, Felix, and Marcel Dominik Wever. “Replacing the Ex-Def Baseline in AutoML by Naive AutoML,” 2021.
LibreCat: /record/22914

Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph Neural Networks.”https://doi.org/10.1007/978-3-030-88942-5__In Proceedings of The 24th International Conference on Discovery Science (DS 2021), edited by Carlos Soares and Luis Torgo, 12986:166–80. Lecture Notes in Computer Science. Springer, 2021.https://doi.org/10.1007/978-3-030-88942-5 .
LibreCat: /record/27381
DOI: 10.1007/978-3-030-88942-5
arXiv: 2104.08869

Wever, Marcel Dominik. Automated Machine Learning for Multi-Label Classification, 2021.https://doi.org/10.17619/UNIPB/1-1302 .
LibreCat: /record/27284
DOI: 10.17619/UNIPB/1-1302

Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data.” PAKDD, 2021.
LibreCat: /record/21198

Pfannschmidt, Karlson, and Eyke Hüllermeier. “Learning Choice Functions via Pareto-Embeddings.”https://doi.org/10.1007/978-3-030-58285-2_30__In Lecture Notes in Computer Science. Cham, 2020.https://doi.org/10.1007/978-3-030-58285-2_30 .
LibreCat: /record/19521
DOI: 10.1007/978-3-030-58285-2_30

Damke, Clemens, Vitaly Melnikov, and Eyke Hüllermeier. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.” In Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), edited by Sinno Jialin Pan and Masashi Sugiyama, 129:49–64. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR, 2020.
LibreCat: /record/19953
arXiv: 2007.00346

Bengs, Viktor, and Eyke Hüllermeier. “Preselection Bandits.” In International Conference on Machine Learning, 778–87, 2020.
LibreCat: /record/21534

Bengs, Viktor, and Eyke Hüllermeier. “Multi-Armed Bandits with Censored Consumption of Resources.” ArXiv:2011.00813, 2020.
LibreCat: /record/21536

Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Extreme Algorithm Selection with Dyadic Feature Representation.” In Discovery Science, 2020.
LibreCat: /record/17407

Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Hybrid Ranking and Regression for Algorithm Selection.” In KI 2020: Advances in Artificial Intelligence, 2020.
LibreCat: /record/17408

Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, Felix Mohr, and Eyke Hüllermeier. “AutoML for Predictive Maintenance: One Tool to RUL Them All.”https://doi.org/10.1007/978-3-030-66770-2_8__In Proceedings of the ECMLPKDD 2020, 2020.https://doi.org/10.1007/978-3-030-66770-2_8 .
LibreCat: /record/17424
DOI: 10.1007/978-3-030-66770-2_8

Heid, Stefan Helmut, Marcel Dominik Wever, and Eyke Hüllermeier. “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction.” Journal of Data Mining and Digital Humanities. episciences, n.d.
LibreCat: /record/17605

Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Towards Meta-Algorithm Selection.” In Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.
LibreCat: /record/20306

El Mesaoudi-Paul, Adil, Dimitri Weiß, Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach.”https://doi.org/10.1007/978-3-030-53552-0_22__In Learning and Intelligent Optimization. LION 2020., 12096:216–32. Lecture Notes in Computer Science. Cham: Springer, 2020.https://doi.org/10.1007/978-3-030-53552-0_22 .
LibreCat: /record/18014
DOI: 10.1007/978-3-030-53552-0_22

El Mesaoudi-Paul, Adil, Viktor Bengs, and Eyke Hüllermeier. “Online Preselection with Context Information under the Plackett-Luce  Model.” ArXiv:2002.04275, n.d.
LibreCat: /record/18017

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.
LibreCat: /record/18276

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.
LibreCat: /record/16725

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.
LibreCat: /record/15629

Wever, Marcel Dominik, Lorijn van Rooijen, and Heiko Hamann. “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.”https://doi.org/10.1162/evco_a_00266__Evolutionary Computation 28, no. 2 (2020): 165–193.https://doi.org/10.1162/evco_a_00266 .
LibreCat: /record/15025
DOI: 10.1162/evco_a_00266

Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Learning Choice Functions: Concepts and Architectures.” ArXiv:1901.10860, 2019.
LibreCat: /record/19523

Merten, Marie-Luis, Nina Seemann, and Marcel Dominik Wever. “Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff.” Niederdeutsches Jahrbuch, no. 142 (2019): 124–46.
LibreCat: /record/17565

Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak Convergence Theory.” ArXiv:1903.09864, 2019.
LibreCat: /record/18018

Wever, Marcel Dominik, Felix Mohr, Eyke Hüllermeier, and Alexander Hetzer. “Towards Automated Machine Learning for Multi-Label Classification,” 2019.
LibreCat: /record/8868

Tagne, V. K., S. Fotso, L. A.  Fono, and Eyke Hüllermeier. “Choice Functions Generated by Mallows and Plackett–Luce Relations.” New Mathematics and Natural Computation 15, no. 2 (2019): 191–213.
LibreCat: /record/10578

Couso, Ines, Christian Borgelt, Eyke Hüllermeier, and Rudolf Kruse. “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning.”https://doi.org/10.1109/mci.2018.2881642__IEEE Computational Intelligence Magazine, 2019, 31–44.https://doi.org/10.1109/mci.2018.2881642 .
LibreCat: /record/15001
DOI: 10.1109/mci.2018.2881642

Waegeman, Willem, Krzysztof Dembczynski, and Eyke Hüllermeier. “Multi-Target Prediction: A Unifying View on Problems and Methods.”https://doi.org/10.1007/s10618-018-0595-5__Data Mining and Knowledge Discovery 33, no. 2 (2019): 293–324.https://doi.org/10.1007/s10618-018-0595-5 .
LibreCat: /record/15002
DOI: 10.1007/s10618-018-0595-5

Mortier, Thomas, Marek Wydmuch, Krzysztof Dembczynski, Eyke Hüllermeier, and Willem Waegeman. “Set-Valued Prediction in Multi-Class Classification.” In 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.
LibreCat: /record/15003

Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Feature Selection for Analogy-Based Learning to Rank.”https://doi.org/10.1007/978-3-030-33778-0_22__In Discovery Science. Cham, 2019.https://doi.org/10.1007/978-3-030-33778-0_22 .
LibreCat: /record/15004
DOI: 10.1007/978-3-030-33778-0_22

Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Analogy-Based Preference Learning with Kernels.”https://doi.org/10.1007/978-3-030-30179-8_3__In KI 2019: Advances in Artificial Intelligence. Cham, 2019.https://doi.org/10.1007/978-3-030-30179-8_3 .
LibreCat: /record/15005
DOI: 10.1007/978-3-030-30179-8_3

Nguyen, Vu-Linh, Sébastien Destercke, and Eyke Hüllermeier. “Epistemic Uncertainty Sampling.”https://doi.org/10.1007/978-3-030-33778-0_7__In Discovery Science. Cham, 2019.https://doi.org/10.1007/978-3-030-33778-0_7 .
LibreCat: /record/15006
DOI: 10.1007/978-3-030-33778-0_7

Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA.”https://doi.org/10.1016/j.jmva.2019.02.017__In Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019.https://doi.org/10.1016/j.jmva.2019.02.017 .
LibreCat: /record/15007
DOI: 10.1016/j.jmva.2019.02.017

Epple, Nico, Simone Dari, Ludwig Drees, Valentin Protschky, and Andreas Riener. “Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries.”https://doi.org/10.1109/ivs.2019.8814100__In 2019 IEEE Intelligent Vehicles Symposium (IV), 2019.https://doi.org/10.1109/ivs.2019.8814100 .
LibreCat: /record/15009
DOI: 10.1109/ivs.2019.8814100

Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking.” In Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, edited by Frank Hoffmann, Eyke Hüllermeier, and Ralf Mikut, 135–46. KIT Scientific Publishing, Karlsruhe, 2019.
LibreCat: /record/15011

Brinker, Klaus, and Eyke Hüllermeier. “A Reduction of Label Ranking to Multiclass Classification.” In Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany, 2019.
LibreCat: /record/15013

Hüllermeier, Eyke, Ines Couso, and Sebastian Diestercke. “Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants.” In Proceedings SUM 2019, International Conference on Scalable Uncertainty Management, 2019.
LibreCat: /record/15014

Henzgen, Sascha, and Eyke Hüllermeier. “Mining Rank Data.”https://doi.org/10.1145/3363572__ACM Transactions on Knowledge Discovery from Data, 2019, 1–36.https://doi.org/10.1145/3363572 .
LibreCat: /record/15015
DOI: 10.1145/3363572

Bengs, Viktor, Matthias Eulert, and Hajo Holzmann. “Asymptotic Confidence Sets for the Jump Curve in Bivariate Regression Problems.”https://doi.org/10.1016/j.jmva.2019.02.017__Journal of Multivariate Analysis, 2019, 291–312.https://doi.org/10.1016/j.jmva.2019.02.017 .
LibreCat: /record/14027
DOI: 10.1016/j.jmva.2019.02.017

Bengs, Viktor, and Hajo Holzmann. “Adaptive Confidence Sets for Kink Estimation.”https://doi.org/10.1214/19-ejs1555__Electronic Journal of Statistics, 2019, 1523–79.https://doi.org/10.1214/19-ejs1555 .
LibreCat: /record/14028
DOI: 10.1214/19-ejs1555

Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier. “From Automated to On-The-Fly Machine Learning.” In INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, 273–74. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft Für Informatik. Bonn: Gesellschaft für Informatik e.V., 2019.
LibreCat: /record/13132

Wever, Marcel Dominik, Felix Mohr, Alexander Tornede, and Eyke Hüllermeier. “Automating Multi-Label Classification Extending ML-Plan,” 2019.
LibreCat: /record/10232

Rohlfing, Katharina, Giuseppe Leonardi, Iris Nomikou, Joanna Rczaszek-Leonardi, and Eyke Hüllermeier. “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches.”https://doi.org/10.1109/TCDS.2019.2892991__IEEE Transactions on Cognitive and Developmental Systems, 2019.https://doi.org/10.1109/TCDS.2019.2892991 .
LibreCat: /record/20243
DOI: 10.1109/TCDS.2019.2892991