Dr. rer. nat. Marcel Wever
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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 31 - 57 out of 57
2021
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
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
Tornede, A., Wever, M., Werner, S., Mohr, F., & Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. In Proceedings of The 12th Asian Conference on Machine Learning (Vol. 129, pp. 737-752). (Proceedings of Machine Learning Research). https://proceedings.mlr.press/v129/tornede20a.html
Tornede, A., Wever, M., & Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. (4th Workshop on Meta-Learning at NeurIPS 2020). Advance online publication. http://arxiv.org/abs/2011.08784v1
Wever, M., Tornede, A., Mohr, F., & Hüllermeier, E. (2020). LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-label Classification. In M. R. Berthold, A. Feelders, & G. Krempl (Eds.), Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Proceedings (pp. 561-573). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12080 LNCS). Springer. https://doi.org/10.1007/978-3-030-44584-3_44
Wever, M., Van Rooijen, L., & Hamann, H. (2020). Multioracle coevolutionary learning of requirements specifications from examples in on-the-fly markets. Evolutionary computation, 28(2), 165-193. https://doi.org/10.1162/evco_a_00266
2019
Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (2019). From Automated to On-The-Fly Machine Learning. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik–Informatik für Gesellschaft https://dl.gi.de/handle/20.500.12116/24989
Tornede, A., Wever, M. D., & Hüllermeier, E. (2019). Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In 29th Workshop Computational Intelligence https://ris.uni-paderborn.de/download/15011/17060/ci_workshop_tornede.pdf
Wever, M. D., Mohr, F., Tornede, A., & Hüllermeier, E. (2019). Automating Multi-Label Classification Extending ML-Plan. In ICML 2019 Workshop AutoML https://ris.uni-paderborn.de/download/10232/13177/Automating_MultiLabel_Classification_Extending_ML-Plan.pdf
2018
Mohr, F., Wever, M., & Hüllermeier, E. (2018). Automated Machine Learning Service Composition. Computing Research Repository (CoRR), September 2018 . https://doi.org/10.48550/arXiv.1809.00486
Mohr, F., Wever, M., & Hüllermeier, E. (2018). ML-Plan: Automated machine learning via hierarchical planning. Machine learning, 107(8-10), 1495-1515. https://doi.org/10.1007/s10994-018-5735-z
Mohr, F., Wever, M., & Hüllermeier, E. (2018). On-the-Fly Service Construction with Prototypes. In Proceedings - 2018 IEEE International Conference on Services Computing, SCC 2018 - Part of the 2018 IEEE World Congress on Services (pp. 225-232). Article 8456422 Institution of Electrical Engineers (IEE). https://doi.org/10.1109/SCC.2018.00036
Mohr, F., Lettmann, T. (Ed.), Hüllermeier, E., & Wever, M. D., (TRANS.) (2018). Programmatic Task Network Planning. In P. Bercher, D. Höller, S. Biundo, & R. Alford (Eds.), Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (pp. 31-39) https://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop08/docs/Mohr18ProgrammaticPlanning.pdf
Mohr, F., Wever, M., & Hüllermeier, E. (2018). Reduction stumps for multi-class classification. In A. Siebes, W. Duivesteijn, & A. Ukkonen (Eds.), Advances in Intelligent Data Analysis XVII - 17th International Symposium, IDA 2018, Proceedings (pp. 225-237). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11191 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-01768-2_19
Mohr, F., Wever, M., Hüllermeier, E., & Faez, A. (2018). Towards the Automated Composition of Machine Learning Services. In 2018 IEEE International Conference on Services Computing (SCC) (pp. 241-244). Article 8456425 Institution of Electrical Engineers (IEE). https://doi.org/10.1109/SCC.2018.00039
Wever, M., Mohr, F., & Hüllermeier, E. (2018). Automated Multi-Label Classification based on ML-Plan. Computing Research Repository (CoRR), November 2018. https://doi.org/10.48550/arXiv.1811.04060
Wever, M., Mohr, F., & Hüllermeier, E. (2018). Ensembles of evolved nested dichotomies for classification. In GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference (pp. 561-568). (GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/3205455.3205562
Wever, M., Mohr, F., & Hüllermeier, E. (2018). ML-Plan for Unlimited-Length Machine Learning Pipelines. Paper presented at International Workshop on Automatic Machine Learning 2018, Stockholm, Sweden. https://ris.uni-paderborn.de/download/3852/3853
2017
Wever, M., Rooijen, L. V., & Hamann, H. (2017). Active coevolutionary learning of requirements specifications from examples. In GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference (pp. 1327-1334). (Proceedings of the Genetic and Evolutionary Computation Conference). https://doi.org/10.1145/3071178.3071258
Wever, M. D., Mohr, F. (Ed.), & Hüllermeier, E. (Ed.) (2017). Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization: 27th Workshop Computational Intelligence. 149-166. https://publikationen.bibliothek.kit.edu/1000074341/4643874
