Hanselle, Jonas Manuel, et al. “Configuration and Evaluation.”10.5281/zenodo.8068466__On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104, doi:10.5281/zenodo.8068466.
LibreCat: /record/45884
DOI: 10.5281/zenodo.8068466

Haake, Claus-Jochen, et al. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Heinz Nixdorf Institut, Universität Paderborn, 2023, doi:10.17619/UNIPB/1-1797.
LibreCat: /record/45863
DOI: 10.17619/UNIPB/1-1797

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

Fehring, Lukass, et al. “HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection.” Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, 2022.
LibreCat: /record/34103
arXiv: 2210.17341

Dreßler, Kevin, et al. “ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-Expression Learning.”10.1145/3511095.3531287__Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia, 2022, doi:10.1145/3511095.3531287.
LibreCat: /record/31806
DOI: 10.1145/3511095.3531287

Tornede, Alexander, et al. “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

Tornede, Alexander, et al. “Algorithm Selection on a Meta Level.” Machine Learning, 2022.
LibreCat: /record/30865
arXiv: 2107.09414

Gevers, Karina, et al. “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, Springer Science and Business Media LLC, 2022, doi:10.1007/s40194-022-01339-9.
LibreCat: /record/33090
DOI: 10.1007/s40194-022-01339-9

Sharma, Arnab, et al. “MLCHECK–Property-Driven Testing of Machine Learning Classifiers.” Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE.
LibreCat: /record/28350

Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.”10.1109/tpami.2021.3051276__IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, pp. 1–1, doi:10.1109/tpami.2021.3051276.
LibreCat: /record/21004
DOI: 10.1109/tpami.2021.3051276

Mohr, Felix, et al. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE.
LibreCat: /record/21092

Tornede, Tanja, et al. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” Proceedings of the Genetic and Evolutionary Computation Conference, 2021.
LibreCat: /record/21570

Hüllermeier, Eyke, et al. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. 2021.
LibreCat: /record/22913

Tornede, Tanja, et al. “Towards Green Automated Machine Learning: Status Quo and Future Directions.” ArXiv:2111.05850, 2021.
LibreCat: /record/30866
arXiv: 2111.05850

Hanselle, Jonas Manuel, et al. Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. 2021.
LibreCat: /record/21198

Tornede, Alexander, et al. “Extreme Algorithm Selection with Dyadic Feature Representation.” Discovery Science, 2020.
LibreCat: /record/17407

Hanselle, Jonas Manuel, et al. “Hybrid Ranking and Regression for Algorithm Selection.” KI 2020: Advances in Artificial Intelligence, 2020.
LibreCat: /record/17408

Tornede, Tanja, et al. “AutoML for Predictive Maintenance: One Tool to RUL Them All.”10.1007/978-3-030-66770-2_8__Proceedings of the ECMLPKDD 2020, 2020, doi:10.1007/978-3-030-66770-2_8.
LibreCat: /record/17424
DOI: 10.1007/978-3-030-66770-2_8

Tornede, Alexander, et al. “Towards Meta-Algorithm Selection.” Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.
LibreCat: /record/20306

Tornede, Alexander, et al. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” ACML 2020, 2020.
LibreCat: /record/18276