Heid SH, Wever MD, Hüllermeier E. Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. Journal of Data Mining and Digital Humanities.
LibreCat: /record/17605

Tornede A, Wever MD, Hüllermeier E. Towards Meta-Algorithm Selection. In: Workshop MetaLearn 2020 @ NeurIPS 2020. ; 2020.
LibreCat: /record/20306

El Mesaoudi-Paul A, Weiß D, Bengs V, Hüllermeier E, Tierney K. Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In: Learning and Intelligent Optimization. LION 2020. Vol 12096. Lecture Notes in Computer Science. Cham: Springer; 2020:216-232. doi:10.1007/978-3-030-53552-0_22
LibreCat: /record/18014
DOI: 10.1007/978-3-030-53552-0_22

El Mesaoudi-Paul A, Bengs V, Hüllermeier E. Online Preselection with Context Information under the Plackett-Luce  Model. arXiv:200204275.
LibreCat: /record/18017

Tornede A, Wever MD, Werner S, Mohr F, Hüllermeier E. Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. In: ACML 2020. ; 2020.
LibreCat: /record/18276

Richter C, Hüllermeier E, Jakobs M-C, Wehrheim H. Algorithm Selection for Software Validation Based on Graph Kernels. Journal of Automated Software Engineering.
LibreCat: /record/16725

Wever MD, Tornede A, Mohr F, Hüllermeier E. LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. In: Springer.
LibreCat: /record/15629

Wever MD, van Rooijen L, Hamann H. Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary Computation. 2020;28(2):165–193. doi:10.1162/evco_a_00266
LibreCat: /record/15025
DOI: 10.1162/evco_a_00266

Pfannschmidt K, Gupta P, Hüllermeier E. Learning Choice Functions: Concepts and Architectures. arXiv:190110860. 2019.
LibreCat: /record/19523

Merten M-L, Seemann N, Wever MD. Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. Niederdeutsches Jahrbuch. 2019;(142):124-146.
LibreCat: /record/17565

Bengs V, Holzmann H. Uniform approximation in classical weak convergence theory. arXiv:190309864. 2019.
LibreCat: /record/18018

Wever MD, Mohr F, Hüllermeier E, Hetzer A. Towards Automated Machine Learning for Multi-Label Classification. In: ; 2019.
LibreCat: /record/8868

Tagne VK, Fotso S, Fono LA, Hüllermeier E. Choice Functions Generated by Mallows and Plackett–Luce Relations. New Mathematics and Natural Computation. 2019;15(2):191-213.
LibreCat: /record/10578

Couso I, Borgelt C, Hüllermeier E, Kruse R. Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Computational Intelligence Magazine. 2019:31-44. doi:10.1109/mci.2018.2881642
LibreCat: /record/15001
DOI: 10.1109/mci.2018.2881642

Waegeman W, Dembczynski K, Hüllermeier E. Multi-target prediction: a unifying view on problems and methods. Data Mining and Knowledge Discovery. 2019;33(2):293-324. doi:10.1007/s10618-018-0595-5
LibreCat: /record/15002
DOI: 10.1007/s10618-018-0595-5

Mortier T, Wydmuch M, Dembczynski K, Hüllermeier E, Waegeman W. 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 M, Hüllermeier E. Feature Selection for Analogy-Based Learning to Rank. In: Discovery Science. Cham; 2019. doi:10.1007/978-3-030-33778-0_22
LibreCat: /record/15004
DOI: 10.1007/978-3-030-33778-0_22

Ahmadi Fahandar M, Hüllermeier E. Analogy-Based Preference Learning with Kernels. In: KI 2019: Advances in Artificial Intelligence. Cham; 2019. doi:10.1007/978-3-030-30179-8_3
LibreCat: /record/15005
DOI: 10.1007/978-3-030-30179-8_3

Nguyen V-L, Destercke S, Hüllermeier E. Epistemic Uncertainty Sampling. In: Discovery Science. Cham; 2019. doi:10.1007/978-3-030-33778-0_7
LibreCat: /record/15006
DOI: 10.1007/978-3-030-33778-0_7

Melnikov V, Hüllermeier E. 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. doi:10.1016/j.jmva.2019.02.017
LibreCat: /record/15007
DOI: 10.1016/j.jmva.2019.02.017

Epple N, Dari S, Drees L, Protschky V, Riener A. Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries. In: 2019 IEEE Intelligent Vehicles Symposium (IV). ; 2019. doi:10.1109/ivs.2019.8814100
LibreCat: /record/15009
DOI: 10.1109/ivs.2019.8814100

Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In: Hoffmann F, Hüllermeier E, Mikut R, eds. Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019. KIT Scientific Publishing, Karlsruhe; 2019:135-146.
LibreCat: /record/15011

Brinker K, Hüllermeier E. 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 E, Couso I, Diestercke S. 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 S, Hüllermeier E. Mining Rank Data. ACM Transactions on Knowledge Discovery from Data. 2019:1-36. doi:10.1145/3363572
LibreCat: /record/15015
DOI: 10.1145/3363572

Bengs V, Eulert M, Holzmann H. Asymptotic confidence sets for the jump curve in bivariate regression problems. Journal of Multivariate Analysis. 2019:291-312. doi:10.1016/j.jmva.2019.02.017
LibreCat: /record/14027
DOI: 10.1016/j.jmva.2019.02.017

Bengs V, Holzmann H. Adaptive confidence sets for kink estimation. Electronic Journal of Statistics. 2019:1523-1579. doi:10.1214/19-ejs1555
LibreCat: /record/14028
DOI: 10.1214/19-ejs1555

Mohr F, Wever MD, Tornede A, Hüllermeier E. From Automated to On-The-Fly Machine Learning. In: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik. Bonn: Gesellschaft für Informatik e.V.; 2019:273-274.
LibreCat: /record/13132

Wever MD, Mohr F, Tornede A, Hüllermeier E. Automating Multi-Label Classification Extending ML-Plan. In: ; 2019.
LibreCat: /record/10232

Rohlfing K, Leonardi G, Nomikou I, Rczaszek-Leonardi J, Hüllermeier E. Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems. Published online 2019. doi:10.1109/TCDS.2019.2892991
LibreCat: /record/20243
DOI: 10.1109/TCDS.2019.2892991

Mohr F, Wever MD, Hüllermeier E, Faez A. (WIP) Towards the Automated Composition of Machine Learning Services. In: SCC. San Francisco, CA, USA: IEEE; 2018. doi:10.1109/SCC.2018.00039
LibreCat: /record/2479
DOI: 10.1109/SCC.2018.00039

Pfannschmidt K, Gupta P, Hüllermeier E. Deep Architectures for Learning Context-dependent Ranking Functions. arXiv:180305796. 2018.
LibreCat: /record/19524

Mohr F, Lettmann T, Hüllermeier E, Wever MD. Programmatic Task Network Planning. In: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning. AAAI; 2018:31-39.
LibreCat: /record/2857

Ramaswamy A, Bhatnagar S. Stability of stochastic approximations with “controlled markov” noise and temporal difference learning. IEEE Transactions on Automatic Control. 2018;64(6):2614-2620.
LibreCat: /record/24150

Demirel B, Ramaswamy A, Quevedo DE, Karl H. Deepcas: A deep reinforcement learning algorithm for control-aware scheduling. IEEE Control Systems Letters. 2018;2(4):737-742.
LibreCat: /record/24151

Mohr F, Wever MD, Hüllermeier E. On-The-Fly Service Construction with Prototypes. In: SCC. San Francisco, CA, USA: IEEE Computer Society; 2018. doi:10.1109/SCC.2018.00036
LibreCat: /record/2471
DOI: 10.1109/SCC.2018.00036

Melnikov V, Hüllermeier E. On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning. 2018. doi:10.1007/s10994-018-5733-1
LibreCat: /record/3402
DOI: 10.1007/s10994-018-5733-1

Mohr F, Wever MD, Hüllermeier E. ML-Plan: Automated Machine Learning via Hierarchical Planning. Machine Learning. Published online 2018:1495-1515. doi:10.1007/s10994-018-5735-z
LibreCat: /record/3510
DOI: 10.1007/s10994-018-5735-z

Mohr F, Wever MD, Hüllermeier E. Reduction Stumps for Multi-Class Classification. In: Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands. doi:10.1007/978-3-030-01768-2_19
LibreCat: /record/3552
DOI: 10.1007/978-3-030-01768-2_19

Wever MD, Mohr F, Hüllermeier E. ML-Plan for Unlimited-Length Machine Learning Pipelines. In: ICML 2018 AutoML Workshop. ; 2018.
LibreCat: /record/3852

Wever MD, Mohr F, Hüllermeier E. 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: ACM; 2018. doi:10.1145/3205455.3205562
LibreCat: /record/2109
DOI: 10.1145/3205455.3205562

Wever MD, Mohr F, Hüllermeier E. Automated Multi-Label Classification based on ML-Plan. Published online 2018.
LibreCat: /record/17713

Mohr F, Wever MD, Hüllermeier E. Automated machine learning service composition. Published online 2018.
LibreCat: /record/17714

Graf H. Ranking of Classification Algorithms in AutoML. Universität Paderborn; 2018.
LibreCat: /record/5693

Scheibl M. Learning about Learning Curves from Dataset Properties. Universität Paderborn; 2018.
LibreCat: /record/5936

Schäfer D, Hüllermeier E. Preference-Based Reinforcement Learning Using Dyad Ranking. In: Discovery Science. Cham: Springer International Publishing; 2018:161-175. doi:10.1007/978-3-030-01771-2_11
LibreCat: /record/6423
DOI: 10.1007/978-3-030-01771-2_11

Abiteboul S, Arenas M, Barceló P, et al., eds. Research Directions for Principles of Data Management. Vol 7.; 2018:1-29.
LibreCat: /record/10591

Couso I, Hüllermeier E. Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In: Mostaghim S, Nürnberger A, Borgelt C, eds. Frontiers in Computational Intelligence. Springer; 2018:31-46.
LibreCat: /record/10783

Schäfer D, Hüllermeier E. Dyad ranking using Plackett-Luce models based on joint feature representations. Machine Learning. 2018;107(5):903-941.
LibreCat: /record/16038

Ahmadi Fahandar M, Hüllermeier E. Learning to Rank Based on Analogical Reasoning. In: Proc. 32 Nd AAAI Conference on Artificial Intelligence (AAAI). ; 2018:2951-2958.
LibreCat: /record/10145