Leibniz Universität Hannover Fakultät Fakultät für Elektrotechnik und Informatik
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Institut für Künstliche Intelligenz
 
Institut
Marius Lindauer
Prof. Dr. rer. nat. Marius Lindauer
Telefon
+49 511 762 5301
E-Mail
m.lindauer@ai.uni-hannover.de
Adresse
Welfengarten 1
30167 Hannover
Gebäude
1101
Raum
a419
Prof. Dr. rer. nat. Marius Lindauer
Telefon
+49 511 762 5301
E-Mail
m.lindauer@ai.uni-hannover.de
Adresse
Welfengarten 1
30167 Hannover
Gebäude
1101
Raum
a419

In recent years, AI achieved impressive results in different fields, incl. in computer vision, natural language processing and reinforcement learning. These breakthroughs show how AI will influence and change our daily lives, business and even research in many aspects. With the advent of deep learning and also traditional AI methods, such as AI planning, SAT solving or evolutionary algorithms, a multitude of different techniques are available these days. However, applying these techniques is challenging, and even experienced AI developers are faced with several difficult design decisions, making the development of new AI applications a tedious, error-prone and time-consuming task. Therefore, we develop new approaches to increase efficiency in AI application development by reducing the required expert knowledge, improving development time and reducing chances of error. We do this with democratization of AI and social responsibility in mind.

Research Interests

Actually, I'm interested in many topics related to AutoML, machine learning, AI and interdisciplinary applications of these. Here are some selected topics:

  • Green-AutoML
  • Human-centered AutoML
  • Dynamic Algorithm Configuration
  • Generalization of Reinforcement Learning
  • Applications to production or health/medicine

Curriculum Vitae

Publications

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2023

Benjamins, C., Eimer, T., Schubert, F. G., Mohan, A., Döhler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (Angenommen/Im Druck). Contextualize Me -- The Case for Context in Reinforcement Learning. Beitrag in European Workshop on Reinforcement Learning 2023, Brüssel.
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Benjamins, C., Eimer, T., Schubert, F. G., Mohan, A., Döhler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (Angenommen/Im Druck). Contextualize Me – The Case for Context in Reinforcement Learning. Transactions on Machine Learning Research. https://doi.org/10.48550/arXiv.2202.04500
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Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Angenommen/Im Druck). Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. in AutoML Conference 2023 PMLR.
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Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Angenommen/Im Druck). Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. in GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference Companion Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation (SIGEVO).
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Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Thomas, J., Ullmann, T., Becker, M., Boulesteix, A-L., Deng, D., & Lindauer, M. (2023). Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(2), [e1484]. https://doi.org/10.1002/widm.1484
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Denkena, B., Dittrich, M-A., Noske, H., Lange, D., Benjamins, C., & Lindauer, M. (2023). Application of machine learning for fleet-based condition monitoring of ball screw drives in machine tools. The international journal of advanced manufacturing technology, 127(3-4), 1143-1164. https://doi.org/10.1007/s00170-023-11524-9
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Eimer, T., Lindauer, M., & Raileanu, R. (2023). Hyperparameters in Reinforcement Learning and How to Tune Them. in ICML'23: Proceedings of the 40th International Conference on Machine Learning (S. 9104–9149). [366] https://doi.org/10.48550/arXiv.2306.01324, https://doi.org/10.5555/3618408.3618774
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Eimer, T., Lindauer, M., & Raileanu, R. (Angenommen/Im Druck). Hyperparameters in Reinforcement Learning and How To Tune Them. in Proceeding of the Fortieth International Conference on Machine Learning (Proceeding of the International Conference on Machine Learning).
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Giovanelli, J., Tornede, A., Tornede, T., & Lindauer, M. (2023). Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning.
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Hutter, F., Fuks, L., Lindauer, M., & Awad, N. (2023). Method, device and computer program for producing a strategy for a robot. (Patent Nr. US11628562B2). https://patentimages.storage.googleapis.com/f9/b3/d5/7596bf6bb838dd/US11628562.pdf
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Loni, M., Mohan, A., Asadi, M., & Lindauer, M. (Angenommen/Im Druck). Learning Activation Functions for Sparse Neural Networks. in Second International Conference on Automated Machine Learning PMLR. https://arxiv.org/abs/2305.10964
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Mallik, N., Bergman, E., Hvarfner, C., Stoll, D., Janowski, M., Lindauer, M., Nardi, L., & Hutter, F. (2023). PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning. in Proceedings of the international Conference on Neural Information Processing Systems (NeurIPS) https://doi.org/10.48550/arXiv.2306.12370
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Mohan, A., Zhang, A., & Lindauer, M. (2023). A Patterns Framework for Incorporating Structure in Deep Reinforcement Learning. https://openreview.net/forum?id=KkKWsPLlAx&referrer=%5BAuthor%20Console%5D(%2Fgroup%3Fid%3DEWRL%2F2023%2FWorkshop%2FAuthors%23your-submissions)
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Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A., & Lindauer, M. (Angenommen/Im Druck). AutoRL Hyperparameter Landscapes. in Second International Conference on Automated Machine Learning PMLR. https://doi.org/10.48550/arXiv.2304.02396
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Mohan, A., Zhang, A., & Lindauer, M. (2023). Structure in Reinforcement Learning: A Survey and Open Problems. (Journal of Artificial Intelligence Research).
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Neutatz, F., Lindauer, M., & Abedjan, Z. (2023). AutoML in Heavily Constrained Applications. VLDB Journal. https://doi.org/10.48550/arXiv.2306.16913, https://doi.org/10.1007/s00778-023-00820-1
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Ruhkopf, T., Mohan, A., Deng, D., Tornede, A., Hutter, F., & Lindauer, M. (2023). MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information. Transactions on Machine Learning Research. https://openreview.net/forum?id=5aYGXxByI6
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Schubert, F., Benjamins, C., Döhler, S., Rosenhahn, B., & Lindauer, M. (2023). POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. Transactions on Machine Learning Research. https://doi.org/10.48550/arXiv.2205.11357
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Segel, S., Graf, H., Tornede, A., Bischl, B., & Lindauer, M. (Angenommen/Im Druck). Symbolic Explanations for Hyperparameter Optimization. in AutoML Conference 2023 PMLR. https://doi.org/10.5281/zenodo.8123425
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Shoaib, M., Kotthoff, L., Lindauer, M., & Kant, S. (2023). AutoML: advanced tool for mining multivariate plant traits. Trends in Plant Science, 28(12), 1451-1452. https://doi.org/10.1016/j.tplants.2023.09.008
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