Hanselle, J. M., Hüllermeier, E., Mohr, F., Ngonga Ngomo, A.-C., Sherif, M., Tornede, A., &; Wever, M. D. (2023). Configuration and Evaluation. In C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, &; H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol. 412, pp. 85–104). Heinz Nixdorf Institut, Universität Paderborn.https://doi.org/10.5281/zenodo.8068466 
LibreCat: /record/45884
DOI: 10.5281/zenodo.8068466

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

Schede, E., Brandt, J., Tornede, A., Wever, M. D., Bengs, V., Hüllermeier, E., &; Tierney, K. (2022). A Survey of Methods for Automated Algorithm Configuration. In arXiv:2202.01651.
LibreCat: /record/30868
arXiv: 2202.01651

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

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

Tornede, A., Gehring, L., Tornede, T., Wever, M. D., &; Hüllermeier, E. (2022). Algorithm Selection on a Meta Level. In Machine Learning.
LibreCat: /record/30865
arXiv: 2107.09414

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

Wever, M. D., Tornede, A., Mohr, F., &; Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1.https://doi.org/10.1109/tpami.2021.3051276 
LibreCat: /record/21004
DOI: 10.1109/tpami.2021.3051276

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

Tornede, T., Tornede, A., Wever, M. D., &; Hüllermeier, E. (2021). Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference.
LibreCat: /record/21570

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

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

Hanselle, J. M., Tornede, A., Wever, M. D., &; Hüllermeier, E. (2021). Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021), Delhi, India.
LibreCat: /record/21198

Tornede, A., Wever, M. D., &; Hüllermeier, E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. Discovery Science. Discovery Science 2020.
LibreCat: /record/17407

Hanselle, J. M., Tornede, A., Wever, M. D., &; Hüllermeier, E. (2020). Hybrid Ranking and Regression for Algorithm Selection. KI 2020: Advances in Artificial Intelligence. 43rd German Conference on Artificial Intelligence.
LibreCat: /record/17408

Tornede, T., Tornede, A., Wever, M. D., Mohr, F., &; Hüllermeier, E. (2020). AutoML for Predictive Maintenance: One Tool to RUL Them All. Proceedings of the ECMLPKDD 2020. IOTStream Workshop @ ECMLPKDD 2020.https://doi.org/10.1007/978-3-030-66770-2_8 
LibreCat: /record/17424
DOI: 10.1007/978-3-030-66770-2_8

Tornede, A., Wever, M. D., &; Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. Workshop MetaLearn 2020 @ NeurIPS 2020. Workshop MetaLearn 2020 @ NeurIPS 2020, Online.
LibreCat: /record/20306

Tornede, A., Wever, M. D., Werner, S., Mohr, F., &; Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. ACML 2020. 12th Asian Conference on Machine Learning, Bangkok, Thailand.
LibreCat: /record/18276

Wever, M. D., Tornede, A., Mohr, F., &; Hüllermeier, E. (n.d.). LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Symposium on Intelligent Data Analysis, Konstanz, Germany.
LibreCat: /record/15629

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