Dr. rer. nat. Marcel Wever
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30167 Hannover
30167 Hannover
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Dr. rer. nat. Marcel Wever
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My research is centered around automated machine learning (AutoML) and related topics such as meta-learning, hyperparameter optimization, algorithm configuration and algorithm selection, as well as supervised learning methods. Specifically, I am interested in methods for multi-label classification. Beyond that, my research interests are widespread and include uncertainty quantification, evolutionary machine learning, machine learning in IT security, part of speech tagging, service-oriented software architectures, and (co-)active learning.
Research Interests
- Interactive and Explainable AutoML
- Green AutoML
- Meta-Learning
- Hyperparameter Optimization
- Algorithm Configuration and Algorithm Selection
- Multi-Label Classification
- (Co-)Active learning
Further research interests:
- Uncertainty quantification
- Evolutionary machine learning
- Machine learning in IT security
- Part-of-speech tagging
- Service-oriented software architectures
Curriculum Vitae
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Working Experience
2024 - Present
Postdoctoral Researcher, Leibniz University Hannover2023 - 2024
Expert Consultant for Machine Learning, Fraunhofer IEM2022 - 2024
Transfer Coordinator for Education, Munich Center for Machine Learning2021 - 2024
Postdoctoral Researcher, LMU Munich2017 - 2021
Doctoral Researcher, Paderborn University -
Education
2017 - 2021
Ph.D. Student (Dr. rer. nat) supervised by Prof. Dr. Eyke Hüllermeier, Paderborn University2015 - 2017
Master of Science, Computer Science, Paderborn University2011 - 2015
Bachelor of Science, Computer Science, Paderborn University -
Selected Awards
2022
Outstanding reviewer at NeurIPS 2022.2021
Outstanding reviewer at ICML 2021.2020
Outstanding reviewer at ICML 2020.2020
Frontier Prize for the most visionary contribution at the International Symposium on Intelligent Data Analysis, 2020.2019
Young author award at the Computational Intelligence Workshop, Dortmund, 2019.2017
Young author award at the Computational Intelligence Workshop, Dortmund, 2017.Best paper award for the SBSE/ACO-SI track at the Genetic and Evolutionary Computation Conference (GECCO), 2017.
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Memberships
2020 - Present
Member of the Benchmarking network2019 - Present
Core developer of OpenML2019 - Present
Member of the COSEAL network - Social Media
Publications
Showing results 1 - 30 out of 48
2024
Brandt, J., Wever, M., Bengs, V., & Hüllermeier, E. (2024). Best Arm Identification with Retroactively Increased Sampling Budget for More Resource-Efficient HPO. 3742-3750. Paper presented at Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24, Jeju, Korea, Republic of. https://doi.org/10.24963/ijcai.2024/414
Gupta, P., Wever, M., & Hüllermeier, E. (2024). Information Leakage Detection through Approximate Bayes-optimal Prediction. Advance online publication. https://doi.org/10.48550/arXiv.2401.14283
Herrmann, M., Lange, F. J. D., Eggensperger, K., Casalicchio, G., Wever, M., Feurer, M., Rügamer, D., Hüllermeier, E., Boulesteix, A.-L., & Bischl, B. (2024). Position: Why We Must Rethink Empirical Research in Machine Learning. In Proceedings of the international conference on machine learning Advance online publication. https://doi.org/10.48550/arXiv.2405.02200
Iliadis, D., Wever, M., De baets, B., & Waegeman, W. (2024). Hyperparameter optimization of two-branch neural networks in multi-target prediction. Applied soft computing, 165, 111957. Article 111957. https://doi.org/10.1016/j.asoc.2024.111957
Margraf, V., Wever, M., Gilhuber, S., Tavares, G. M., Seidl, T., & Hüllermeier, E. (2024). ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data. Advance online publication. https://doi.org/10.48550/arXiv.2406.17322
Margraf, V., Wever, M. D., & Hüllermeier, E. (2024). On the Importance of Initialization in Active Learning. Paper presented at The Weaklyy Supervised and Cautious Learning Workshop held in conjunction with 27th European Conference on Artificial Intelligence (ECAI), Santiago de Compostela, Spain.
2023
Brandt, J., Wever, M., Iliadis, D., Bengs, V., & Hüllermeier, E. (2023). Iterative Deepening Hyperband. (CoRR). Advance online publication. https://doi.org/10.48550/arXiv.2302.00511
Hanselle, J., Hüllermeier, E., Mohr, F., Ngomo, A. C. N., Sherif, M. A., Tornede, A., & Wever, M. D. (2023). Configuration and Evaluation. In On-The-Fly Computing -- Individualized IT-services in dynamic markets https://doi.org/10.5281/zenodo.8068466
Merten, M. L., Wever, M., Geierhos, M., Tophinke, D., & Hüllermeier, E. (2023). Annotation uncertainty in the context of grammatical change. International Journal of Corpus Linguistics, 28(3), 430-459. https://doi.org/10.1075/ijcl.20113.mer
Mohr, F., & Wever, M. (2023). Naive automated machine learning. Machine learning, 112(4), 1131-1170. https://doi.org/10.1007/s10994-022-06200-0
Schede, E., Brandt, J., Tornede, A., Wever, M., Bengs, V., Hüllermeier, E., & Tierney, K. (2023). A Survey of Methods for Automated Algorithm Configuration (Extended Abstract). In E. Elkind (Ed.), Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 (pp. 6964-6968). (IJCAI International Joint Conference on Artificial Intelligence; Vol. 2023-August). AAAI Press/International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2023/791
Schubert, D., Gupta, P., & Wever, M. (2023). Meta-learning for Automated Selection of Anomaly Detectors for Semi-supervised Datasets. In B. Crémilleux, S. Hess, & S. Nijssen (Eds.), Advances in Intelligent Data Analysis XXI - 21st International Symposium on Intelligent Data Analysis, IDA 2023, Proceedings (pp. 392-405). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13876 LNCS). https://doi.org/10.1007/978-3-031-30047-9_31
Tornede, A., Gehring, L., Tornede, T., Wever, M., & Hüllermeier, E. (2023). Algorithm selection on a meta level. Machine learning, 112(4), 1253-1286. https://doi.org/10.1007/s10994-022-06161-4
Tornede, T., Tornede, A., Fehring, L., Gehring, L., Graf, H., Hanselle, J., Mohr, F., & Wever, M. (2023). PyExperimenter: Easily distribute experiments and track results. Journal of Open Source Software, 8(84). https://doi.org/10.48550/arXiv.2301.06348, https://doi.org/10.21105/joss.05149
Tornede, T., Tornede, A., Hanselle, J., Mohr, F., Wever, M., & Hüllermeier, E. (2023). Towards Green Automated Machine Learning: Status Quo and Future Directions. Journal of Artificial Intelligence Research, 77, 427-457. https://doi.org/10.1613/jair.1.14340
Wever, M., Özdogan, M., & Hüllermeier, E. (2023). Cooperative Co-Evolution for Ensembles of Nested Dichotomies for Multi-Class Classification. In GECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference (pp. 597-605). (Proceedings of the Genetic and Evolutionary Computation Conference). https://doi.org/10.1145/3583131.3590457
2022
Gevers, K., Tornede, A., Wever, M., 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, 66(10), 2157-2170. https://doi.org/10.1007/s40194-022-01339-9
Hüllermeier, E., Wever, M., Loza Mencia, E., Fürnkranz, J., & Rapp, M. (2022). A flexible class of dependence-aware multi-label loss functions. Machine learning, 111(2), 713-737. https://doi.org/10.1007/s10994-021-06107-2
Schede, E., Brandt, J., Tornede, A., Wever, M., Bengs, V., Hüllermeier, E., & Tierney, K. (2022). A Survey of Methods for Automated Algorithm Configuration. Journal of Artificial Intelligence Research, 75, 425-487. https://doi.org/10.1613/jair.1.13676
2021
Hanselle, J., Tornede, A., Wever, M., & Hüllermeier, E. (2021). Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. In K. Karlapalem, H. Cheng, N. Ramakrishnan, R. K. Agrawal, P. K. Reddy, J. Srivastava, & T. Chakraborty (Eds.), Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Proceedings (pp. 152-163). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12712 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-75762-5_13, https://doi.org/10.1007/978-3-030-75762-5_13
Hüllermeier, E., Mohr, F., Tornede, A., & Wever, M. D. (2021). Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. In ECML/PKDD workshop on Automating Data Science (ADS 2021) https://arxiv.org/abs/2109.04744
Merten, M.-L., Seemann, N., & Wever, M. D. (2021). Grammatikwandel digital-kulturwissenschaftlich erforscht: Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. In Jahrbuch des Vereins für Niederdeutsche Sprachforschung: Jahrgang 2021 (1. ed., pp. 124-146). (Jahrbuch des Vereins für Niederdeutsche Sprachforschung; Vol. 144). Wachholtz, Murmann Publishers.
Mohr, F., Wever, M., Tornede, A., & Hullermeier, E. (2021). Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3055-3066. Article 9347828. https://doi.org/10.1109/tpami.2021.3056950
Mohr, F., & Wever, M. (2021). Replacing the Ex-Def Baseline in AutoML by Naive AutoML. In Proceedings of the 8th ICML Workshop on Automated Machine Learning
Tornede, T., Tornede, A., Wever, M., Mohr, F., & Hüllermeier, E. (2021). AutoML for Predictive Maintenance: One Tool to RUL Them All. In J. Gama, S. Pashami, A. Bifet, M. Sayed-Mouchawe, H. Fröning, F. Pernkopf, G. Schiele, & M. Blott (Eds.), 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 (1 ed., pp. 106–118). (Communications in Computer and Information Science; Vol. 1325). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-66770-2_8
Tornede, T., Tornede, A., Wever, M., & Hüllermeier, E. (2021). Coevolution of remaining useful lifetime estimation pipelines for automated predictive maintenance. In GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference (pp. 368-376). (ACM Conferences). Association for Computing Machinery, Inc. https://doi.org/10.1145/3449639.3459395
Wever, M., Tornede, A., Mohr, F., & Hullermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3037-3054. Article 9321731. https://doi.org/10.1109/TPAMI.2021.3051276
2020
Hanselle, J., Tornede, A., Wever, M., & Hüllermeier, E. (2020). Hybrid ranking and regression for algorithm selection. In U. Schmid, D. Wolter, & F. Klügl (Eds.), KI 2020: Advances in Artificial Intelligence - 43rd German Conference on AI, Proceedings (pp. 59-72). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12325 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58285-2_5
Heid, S., Wever, M., & Hüllermeier, E. (2020). Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. Computing Research Repository (CoRR), abs/2008.01377. https://dblp.org/db/journals/corr/corr2008.html#abs-2008-01377
Tornede, A., Wever, M., & Hüllermeier, E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. In A. Appice, G. Tsoumakas, Y. Manolopoulos, & S. Matwin (Eds.), Discovery Science - 23rd International Conference, DS 2020, Proceedings (pp. 309-324). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12323 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-61527-7_21
