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Dr.  Marcel Dominik Wever

https://orcid.org/0000-0001-9782-6818
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Publications

  1. 2023

  2. PyExperimenter: Easily distribute experiments and track results

    Tornede, T., Tornede, A., Fehring, L., Gehring, L., Graf, H., Hanselle, J., Mohr, F. & Wever, M., 20 Apr 2023, In: Journal of Open Source Software. 8, 84, 3 p.

    Research output: Contribution to journal › Article › Research › peer review

  3. Algorithm selection on a meta level

    Tornede, A., Gehring, L., Tornede, T., Wever, M. & Hüllermeier, E., Apr 2023, In: Machine learning. 112, 4, p. 1253-1286 34 p.

    Research output: Contribution to journal › Article › Research › peer review

  4. Naive automated machine learning

    Mohr, F. & Wever, M., Apr 2023, In: Machine learning. 112, 4, p. 1131-1170 40 p.

    Research output: Contribution to journal › Article › Research › peer review

  5. Cooperative Co-Evolution for Ensembles of Nested Dichotomies for Multi-Class Classification.

    Wever, M., Özdogan, M. & Hüllermeier, E., 2023, GECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference. p. 597-605 9 p. (Proceedings of the Genetic and Evolutionary Computation Conference).

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  6. E-pub ahead of print

    Iterative Deepening Hyperband

    Brandt, J., Wever, M., Iliadis, D., Bengs, V. & Hüllermeier, E., 2023, (E-pub ahead of print) (CoRR).

    Research output: Working paper/Preprint › Preprint

  7. Meta-learning for Automated Selection of Anomaly Detectors for Semi-supervised Datasets.

    Schubert, D., Gupta, P. & Wever, M., 2023, Advances in Intelligent Data Analysis XXI - 21st International Symposium on Intelligent Data Analysis, IDA 2023, Proceedings. Crémilleux, B., Hess, S. & Nijssen, S. (eds.). p. 392-405 14 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13876 LNCS).

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  8. Towards Green Automated Machine Learning: Status Quo and Future Directions

    Tornede, T., Tornede, A., Hanselle, J., Mohr, F., Wever, M. & Hüllermeier, E., 2023, In: Journal of Artificial Intelligence Research. 77, p. 427-457 31 p.

    Research output: Contribution to journal › Article › Research › peer review

  9. 2022

  10. A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials

    Gevers, K., Tornede, A., Wever, M., Schöppner, V. & Hüllermeier, E., Oct 2022, In: Welding in the world. 66, 10, p. 2157-2170 14 p.

    Research output: Contribution to journal › Article › Research › peer review

  11. A flexible class of dependence-aware multi-label loss functions

    Hüllermeier, E., Wever, M., Loza Mencia, E., Fürnkranz, J. & Rapp, M., Feb 2022, In: Machine learning. 111, 2, p. 713-737 25 p.

    Research output: Contribution to journal › Article › Research › peer review

  12. A Survey of Methods for Automated Algorithm Configuration

    Schede, E., Brandt, J., Tornede, A., Wever, M., Bengs, V., Hüllermeier, E. & Tierney, K., 2022, In: Journal of Artificial Intelligence Research. 75, p. 425-487 63 p.

    Research output: Contribution to journal › Review article › Research › peer review

  13. 2021

  14. Grammatikwandel digital-kulturwissenschaftlich erforscht: Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff

    Merten, M., Seemann, N. & Wever, M. D., 22 Dec 2021, Jahrbuch des Vereins für Niederdeutsche Sprachforschung: Jahrgang 2021. 1. ed. p. 124-146 23 p. (Jahrbuch des Vereins für Niederdeutsche Sprachforschung; vol. 144).

    Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review

  15. AutoML for Multi-Label Classification: Overview and Empirical Evaluation

    Wever, M., Tornede, A., Mohr, F. & Hullermeier, E., 1 Sept 2021, In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 43, 9, p. 3037-3054 18 p., 9321731.

    Research output: Contribution to journal › Review article › Research › peer review

  16. Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning

    Mohr, F., Wever, M., Tornede, A. & Hullermeier, E., 1 Sept 2021, In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 43, 9, p. 3055-3066 12 p., 9347828.

    Research output: Contribution to journal › Article › Research › peer review

  17. Coevolution of remaining useful lifetime estimation pipelines for automated predictive maintenance

    Tornede, T., Tornede, A., Wever, M. & Hüllermeier, E., 26 Jun 2021, GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference. p. 368-376 9 p. (ACM Conferences).

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  18. AutoML for Predictive Maintenance: One Tool to RUL Them All

    Tornede, T., Tornede, A., Wever, M., Mohr, F. & Hüllermeier, E., 10 Jan 2021, IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning: Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers. Gama, J., Pashami, S., Bifet, A., Sayed-Mouchawe, M., Fröning, H., Pernkopf, F., Schiele, G. & Blott, M. (eds.). 1 ed. Springer Science and Business Media Deutschland GmbH, p. 106–118 13 p. (Communications in Computer and Information Science; vol. 1325).

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  19. Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data

    Hanselle, J., Tornede, A., Wever, M. & Hüllermeier, E., 2021, Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Proceedings. Karlapalem, K., Cheng, H., Ramakrishnan, N., Agrawal, R. K., Reddy, P. K., Srivastava, J. & Chakraborty, T. (eds.). Springer Science and Business Media Deutschland GmbH, p. 152-163 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12712 LNAI).

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  20. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning

    Hüllermeier, E., Mohr, F., Tornede, A. & Wever, M. D., 2021, ECML/PKDD workshop on Automating Data Science (ADS 2021).

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  21. Replacing the Ex-Def Baseline in AutoML by Naive AutoML

    Mohr, F. & Wever, M., 2021, Proceedings of the 8th ICML Workshop on Automated Machine Learning. 16 p.

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  22. 2020

  23. Towards Meta-Algorithm Selection

    Tornede, A., Wever, M. & Hüllermeier, E., 17 Nov 2020, (E-pub ahead of print) (4th Workshop on Meta-Learning at NeurIPS 2020).

    Research output: Working paper/Preprint › Preprint

  24. Multioracle coevolutionary learning of requirements specifications from examples in on-the-fly markets

    Wever, M., Van Rooijen, L. & Hamann, H., 1 Jun 2020, In: Evolutionary computation. 28, 2, p. 165-193 29 p.

    Research output: Contribution to journal › Article › Research › peer review

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