Leibniz Universität Hannover Faculty Faculty of Electrical Engineering and Computer Science
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Institute of Artificial Intelligence
 
Institute of Artificial Intelligence LUHAI Institute
Marcel Wever
Dr. rer. nat. Marcel Wever
Phone
+49 511 762 12413
Email
m.wever@ai.uni-hannover.de
Address
Welfengarten 1
30167 Hannover
Building
1101
Room
b415
Dr. rer. nat. Marcel Wever
Phone
+49 511 762 12413
Email
m.wever@ai.uni-hannover.de
Address
Welfengarten 1
30167 Hannover
Building
1101
Room
b415

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

  • Working Experience

    2024 - Present
    Postdoctoral Researcher,     Leibniz University Hannover

    2023 - 2024
    Expert Consultant for Machine Learning, Fraunhofer IEM

    2022 - 2024
    Transfer Coordinator for Education, Munich Center for Machine Learning

    2021 - 2024
    Postdoctoral Researcher, LMU Munich

    2017 - 2021
    Doctoral Researcher, Paderborn University

  • Education

    2017 - 2021
    Ph.D. Student (Dr. rer. nat) supervised by Prof. Dr. Eyke Hüllermeier, Paderborn University

    2015 - 2017
    Master of Science, Computer Science, Paderborn University

    2011 - 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.

  • Memberships

    2020 - Present
    Member of the Benchmarking network

    2019 - Present
    Core developer     of OpenML

    2019 - Present
    Member     of the COSEAL network

  • Social Media

    Find me on Google Scholar

    Follow me on Twitter
     

Publications

First 1 2

Showing results 31 - 48 out of 48

2020

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
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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
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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
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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
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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
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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
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Wever, M., Van Rooijen, L., & Hamann, H. (2019). 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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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