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.

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.

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

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.

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 .

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.

A. Tornede, L. Gehring, T. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection on a Meta Level,” Machine Learning. 2022.

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 .

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

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 .

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.

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.

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.

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.

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

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

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.

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 .

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

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.

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.

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 .

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 .

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.

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.

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.

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.

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.

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.

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.

F. Mohr, M. D. Wever, and E. Hüllermeier, “On-The-Fly Service Construction with Prototypes,” in SCC, San Francisco, CA, USA, 2018.

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 .

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.

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

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.

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

F. Mohr, M. D. Wever, and E. Hüllermeier, “Automated machine learning service composition.” 2018.

D. Schäfer and E. Hüllermeier, “Preference-Based Reinforcement Learning Using Dyad Ranking,” in Discovery Science, Cham: Springer International Publishing, 2018, pp. 161–175.

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.

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.

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

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.

A. Jungmann and B. Kleinjohann, “A Holistic and Adaptive Approach for Automated Prototyping of Image Processing Functionality,” in Proceedings of the 21st IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2016, pp. 1--8.

A. Jungmann and B. Kleinjohann, “Automatic Composition of Service-based Image Processing Applications,” in Proceedings of the 13th IEEE International Conference on Services Computing (SCC), 2016, pp. 106--113.

S. Arifulina, M. C. Platenius, F. Mohr, G. Engels, and W. Schäfer, “Market-Specific Service Compositions: Specification and Matching,” in Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet, 2015, pp. 333--340.

M. Becker, S. Lehrig, and S. Becker, “Systematically Deriving Quality Metrics for Cloud Computing Systems,” in Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering, 2015, pp. 169--174.

A. Jungmann and F. Mohr, “An approach towards adaptive service composition in markets of composed services,” Journal of Internet Services and Applications, no. 1, pp. 1–18, 2015.

F. Mohr, “A Metric for Functional Reusability of Services,” in Proceedings of the 14th International Conference on Software Reuse (ICSR), 2015, pp. 298--313.

U. Bubeck and H. Kleine Büning, “Learning Boolean Specifications,” Artificial Intelligence, pp. 246–257, 2015.

A. Jungmann, J. Jatzkowski, and B. Kleinjohann, “Combining Service-oriented Computing with Embedded Systems - A Robotics Case Study,” in Proceedings of the 5th IFIP International Embedded Systems Symposium, 2015.

F. Mohr, A. Jungmann, and H. Kleine Büning, “Automated Online Service Composition,” in Proceedings of the 12th IEEE International Conference on Services Computing (SCC), 2015, pp. 57--64.

A. Jungmann, “On Adaptivity for Automated Composition of Service Functionality,” in Proceedings of the IEEE 11th World Congress on Services (SERVICES), 2015, pp. 329--332.

A. Jungmann and B. Kleinjohann, “Towards Context-Sensitive Service Composition for Service-Oriented Image Processing,” in Proceedings of the 6th International Conference on Cloud Computing Technology and Science (CloudCom), 2014, pp. 755–758.

S. Brangewitz, A. Jungmann, R. Petrlic, and M. C. Platenius, “Towards a Flexible and Privacy-Preserving Reputation System for Markets of Composed Services,” in Proceedings of the 6th International Conferences on Advanced Service Computing (SERVICE COMPUTATION), 2014, pp. 49–57.

F. Mohr and S. Walther, “Template-based Generation of Semantic Services,” in Proceedings of the 14th International Conference on Software Reuse (ICSR), 2014, pp. 188–203.

R. Petrlic, A. Jungmann, M. C. Platenius, W. Schäfer, and C. Sorge, “Security and Privacy Challenges in On-The-Fly Computing,” in Tagungsband der 4. Konferenz Software-Technologien und -Prozesse (STeP 2014), 2014, pp. 131–142.

A. Jungmann, F. Mohr, and B. Kleinjohann, “Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services,” in Proceedings of the 10th World Congress on Services (SERVICES), 2014, pp. 346–353.

A. Jungmann, F. Mohr, and B. Kleinjohann, “Applying Reinforcement Learning for Resolving Ambiguity in Service Composition,” in Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA), 2014, pp. 105–112.

F. Mohr, “Issues of Automated Software Composition in AI Planning,” in Proceedings of the 29th International Conference on Automated Software Engineering (ASE), 2014, pp. 895--898.

A. Jungmann, S. Brangewitz, R. Petrlic, and M. C. Platenius, “Incorporating Reputation Information into Decision-Making Processes in Markets of Composed Services,” International Journal On Advances in Intelligent Systems (IntSys), vol. 7, no. 3&;4, pp. 572--594, 2014.

A. Jungmann, J. Jatzkowski, and B. Kleinjohann, “Evaluation of Color Spaces for Robust Image Segmentation,” in Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP), 2014, pp. 648–655.

F. Mohr, “Estimating Functional Reusability of Services,” in Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC), 2014, pp. 411–418.

F. Mohr and H. Kleine Büning, “Semi-Automated Software Composition Through Generated Components,” in Proceedings of the 15th International Conference on Information Integration and Web-based Applications &; Services (iiWAS), 2013, pp. 676–680.

F. Mohr, T. Lettmann, and H. Kleine Büning, “Reducing Nondeterminism in Automated Service Composition,” in Proceedings of the 6th International Conference on Service Oriented Computing and Applications (SOCA), 2013, pp. 154–161.

A. Jungmann, B. Kleinjohann, and E. Kleinjohann, “Learning Service Recommendations,” International Journal of Business Process Integration and Management, no. 4, pp. 284–297, 2013.

A. Jungmann and B. Kleinjohann, “Learning Recommendation System for Automated Service Composition,” in Proceedings of the 10th IEEE International Conference on Services Computing (SCC), 2013, pp. 97–104.

A. Jungmann and B. Kleinjohann, “Towards the Application of Reinforcement Learning Techniques for Quality-Based Service Selection in Automated Service Composition,” in Proceedings of the 9th IEEE International Conference on Service Computing (SCC), 2012, pp. 701–702.

A. Jungmann and B. Kleinjohann, “Towards an Integrated Service Rating and Ranking Methodology for Quality Based Service Selection in Automatic Service Composition,” in Proceedings of the 4th International Conferences on Advanced Service Computing (SERVICE COMPUTATION), 2012, pp. 43–47.

A. Jungmann, B. Kleinjohann, E. Kleinjohann, and M. Bieshaar, “Efficient Color-Based Image Segmentation and Feature Classification for Image Processing in Embedded Systems,” in Proceedings of the Fourth International Conference on Resource Intensive Applications and Services (INTENSIVE), 2012, pp. 22–29.