J. M. Hanselle et al., “Configuration and Evaluation,” in On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104.
LibreCat: /record/45884
DOI: 10.5281/zenodo.8068466

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

C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023.
LibreCat: /record/45863
DOI: 10.17619/UNIPB/1-1797

E. Schede et al., “A Survey of Methods for Automated Algorithm Configuration,” arXiv:2202.01651. 2022.
LibreCat: /record/30868
arXiv: 2202.01651

L. Fehring, J. M. Hanselle, and A. Tornede, “HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection,” presented at the Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, Baltimore, 2022.
LibreCat: /record/34103
arXiv: 2210.17341

K. Dreßler, M. Sherif, and A.-C. Ngonga Ngomo, “ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning,”10.1145/3511095.3531287__presented at the HT ’22: 33rd ACM Conference on Hypertext and Social Media, Barcelona (Spain), 2022, doi:10.1145/3511095.3531287 .
LibreCat: /record/31806
DOI: 10.1145/3511095.3531287

L. Fehring, Combined Ranking and Regression Trees for Algorithm Selection. Paderborn, 2022.
LibreCat: /record/33033

A. Tornede, V. Bengs, and E. Hüllermeier, “Machine Learning for Online Algorithm Selection under Censored Feedback,” Proceedings of the 36th AAAI Conference on Artificial Intelligence. AAAI, 2022.
LibreCat: /record/30867
arXiv: 2109.06234

A. Tornede, L. Gehring, T. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection on a Meta Level,” Machine Learning. 2022.
LibreCat: /record/30865
arXiv: 2107.09414

K. Gevers, A. Tornede, M. D. Wever, V. Schöppner, and E. Hüllermeier, “A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials,”10.1007/s40194-022-01339-9__Welding in the World, 2022, doi:10.1007/s40194-022-01339-9 .
LibreCat: /record/33090
DOI: 10.1007/s40194-022-01339-9

A. Sharma, C. Demir, A.-C. Ngonga Ngomo, and H. Wehrheim, “MLCHECK–Property-Driven Testing of Machine Learning Classifiers.”
LibreCat: /record/28350

M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label Classification: Overview and Empirical Evaluation,”10.1109/tpami.2021.3051276__IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–1, 2021, doi:10.1109/tpami.2021.3051276 .
LibreCat: /record/21004
DOI: 10.1109/tpami.2021.3051276

F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence.
LibreCat: /record/21092

T. Tornede, A. Tornede, M. D. Wever, and E. Hüllermeier, “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance,” presented at the Genetic and Evolutionary Computation Conference, 2021.
LibreCat: /record/21570

E. Hüllermeier, F. Mohr, A. Tornede, and M. D. Wever, “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” presented at the ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual), 2021.
LibreCat: /record/22913

T. Tornede, A. Tornede, J. M. Hanselle, M. D. Wever, F. Mohr, and E. Hüllermeier, “Towards Green Automated Machine Learning: Status Quo and Future Directions,” arXiv:2111.05850. 2021.
LibreCat: /record/30866
arXiv: 2111.05850

M. D. Wever, Automated Machine Learning for Multi-Label Classification. 2021.
LibreCat: /record/27284
DOI: 10.17619/UNIPB/1-1302

J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data.” 2021.
LibreCat: /record/21198

A. Tornede, M. D. Wever, and E. Hüllermeier, “Extreme Algorithm Selection with Dyadic Feature Representation,” presented at the Discovery Science 2020, 2020.
LibreCat: /record/17407

J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Hybrid Ranking and Regression for Algorithm Selection,” presented at the 43rd German Conference on Artificial Intelligence, 2020.
LibreCat: /record/17408

T. Tornede, A. Tornede, M. D. Wever, F. Mohr, and E. Hüllermeier, “AutoML for Predictive Maintenance: One Tool to RUL Them All,”10.1007/978-3-030-66770-2_8__presented at the IOTStream Workshop @ ECMLPKDD 2020, 2020, doi:10.1007/978-3-030-66770-2_8 .
LibreCat: /record/17424
DOI: 10.1007/978-3-030-66770-2_8

A. Tornede, M. D. Wever, and E. Hüllermeier, “Towards Meta-Algorithm Selection,” presented at the Workshop MetaLearn 2020 @ NeurIPS 2020, Online, 2020.
LibreCat: /record/20306

A. Tornede, M. D. Wever, S. Werner, F. Mohr, and E. Hüllermeier, “Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis,” presented at the 12th Asian Conference on Machine Learning, Bangkok, Thailand, 2020.
LibreCat: /record/18276

M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification,” presented at the Symposium on Intelligent Data Analysis, Konstanz, Germany.
LibreCat: /record/15629

M. D. Wever, L. van Rooijen, and H. Hamann, “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, pp. 165–193, 2020, doi:10.1162/evco_a_00266 .
LibreCat: /record/15025
DOI: 10.1162/evco_a_00266

D. C. Moussalem, Knowledge Graphs for Multilingual Language Translation and Generation. 2020.
LibreCat: /record/16935
DOI: 10.17619/UNIPB/1-980

H. Karl, D. Kundisch, F. Meyer auf der Heide, and H. Wehrheim, “A Case for a New IT Ecosystem: On-The-Fly Computing,”10.1007/s12599-019-00627-x__Business &; Information Systems Engineering, vol. 62, no. 6, pp. 467–481, 2020, doi:10.1007/s12599-019-00627-x .
LibreCat: /record/13770
DOI: 10.1007/s12599-019-00627-x

M. D. Wever, F. Mohr, E. Hüllermeier, and A. Hetzer, “Towards Automated Machine Learning for Multi-Label Classification,” presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany, 2019.
LibreCat: /record/8868

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

A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking,” in Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, Dortmund, 2019, pp. 135–146.
LibreCat: /record/15011

F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “From Automated to On-The-Fly Machine Learning,” in INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, Kassel, 2019, pp. 273–274.
LibreCat: /record/13132

M. D. Wever, F. Mohr, A. Tornede, and E. Hüllermeier, “Automating Multi-Label Classification Extending ML-Plan,” presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA, 2019.
LibreCat: /record/10232

F. Mohr, M. D. Wever, E. Hüllermeier, and A. Faez, “(WIP) Towards the Automated Composition of Machine Learning Services,” in SCC, San Francisco, CA, USA, 2018.
LibreCat: /record/2479
DOI: 10.1109/SCC.2018.00039

F. Mohr, T. Lettmann, E. Hüllermeier, and M. D. Wever, “Programmatic Task Network Planning,” in Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, Delft, Netherlands, 2018, pp. 31–39.
LibreCat: /record/2857

F. Mohr, M. D. Wever, and E. Hüllermeier, “On-The-Fly Service Construction with Prototypes,” in SCC, San Francisco, CA, USA, 2018.
LibreCat: /record/2471
DOI: 10.1109/SCC.2018.00036

F. Mohr, M. D. Wever, and E. Hüllermeier, “ML-Plan: Automated Machine Learning via Hierarchical Planning,”10.1007/s10994-018-5735-z__Machine Learning, pp. 1495–1515, 2018, doi:10.1007/s10994-018-5735-z .
LibreCat: /record/3510
DOI: 10.1007/s10994-018-5735-z

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

M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine Learning Pipelines,” in ICML 2018 AutoML Workshop, Stockholm, Sweden, 2018.
LibreCat: /record/3852

M. D. Wever, F. Mohr, and E. Hüllermeier, “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, 2018.
LibreCat: /record/2109
DOI: 10.1145/3205455.3205562

M. D. Wever, F. Mohr, and E. Hüllermeier, “Automated Multi-Label Classification based on ML-Plan.” Arxiv, 2018.
LibreCat: /record/17713

F. Mohr, M. D. Wever, and E. Hüllermeier, “Automated machine learning service composition.” 2018.
LibreCat: /record/17714

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

M. Scheibl, Learning about learning curves from dataset properties. Universität Paderborn, 2018.
LibreCat: /record/5936

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

M.-C. Jakobs, J. Krämer, D. van Straaten, and T. Lettmann, “Certiû01cation Matters for Service Markets,” in The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION), 2017, pp. 7–12.
LibreCat: /record/115

N. N. Schnitker, Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies. Universität Paderborn, 2017.
LibreCat: /record/5694

A. Hetzer and T. Tornede, Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction. Universität Paderborn, 2017.
LibreCat: /record/5724

M. Czech, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Predicting Rankings of Software Verification Tools,” in Proceedings of the 3rd International Workshop on Software Analytics, 2017, pp. 23–26.
LibreCat: /record/71
DOI: 10.1145/3121257.3121262

M. D. Wever, F. Mohr, and E. Hüllermeier, “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization,” in 27th Workshop Computational Intelligence, Dortmund, 2017.
LibreCat: /record/1180

M. C. Platenius, A. Shaker, M. Becker, E. Hüllermeier, and W. Schäfer, “Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic,” IEEE Transactions on Software Engineering (TSE), presented at ICSE 2017, no. 8, pp. 739–759, 2016.
LibreCat: /record/190
DOI: 10.1109/TSE.2016.2632115