Leibniz Universität Hannover Faculty Faculty of Electrical Engineering and Computer Science
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Institute of Artificial Intelligence
 
InstituteStaff
Marius Lindauer
Prof. Dr. rer. nat. Marius Lindauer
Phone
+49 511 762 5301
Email
m.lindauer@ai.uni-hannover.de
Address
Welfengarten 1
30167 Hannover
Building
1101
Room
a419
Prof. Dr. rer. nat. Marius Lindauer
Phone
+49 511 762 5301
Email
m.lindauer@ai.uni-hannover.de
Address
Welfengarten 1
30167 Hannover
Building
1101
Room
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

Showing entries 1 - 20 out of 93
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2023

Benjamins, C., Eimer, T., Schubert, F. G., Mohan, A., Döhler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (Accepted/In press). Contextualize Me – The Case for Context in Reinforcement Learning. Transactions on Machine Learning Research.
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Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Accepted/In press). 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. (Accepted/In press). 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. (Accepted/In press). 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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Hutter, F., Fuks, L., Lindauer, M., & Awad, N. (2023). Method, device and computer program for producing a strategy for a robot. (Patent No. US11628562B2). https://patentimages.storage.googleapis.com/f9/b3/d5/7596bf6bb838dd/US11628562.pdf
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Loni, M., Mohan, A., Asadi, M., & Lindauer, M. (Accepted/In press). 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://openreview.net/forum?id=uoiwugtpCH
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Mohan, A., Zhang, A., & Lindauer, M. (2023). A Patterns Framework for Incorporating Structure in Deep Reinforcement Learning. In The 16th European Workshop on Reinforcement Learning (EWRL 2023) 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. (Accepted/In press). 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. https://doi.org/10.48550/arXiv.2306.16913
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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. (Accepted/In press). Symbolic Explanations for Hyperparameter Optimization. In AutoML Conference 2023 PMLR. https://doi.org/10.5281/zenodo.8123425
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Theodorakopoulos, D., Manß, C., Stahl, F., & Lindauer, M. (2023). Green-AutoML for Plastic Litter Detection. In Proceedings of the ICLR Workshop on Tackling Climate Change with Machine Learning https://www.climatechange.ai/papers/iclr2023/53
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Tornede, A., Deng, D., Eimer, T., Giovanelli, J., Mohan, A., Ruhkopf, T., Segel, S., Theodorakopoulos, D., Tornede, T., Wachsmuth, H., & Lindauer, M. (2023). AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. https://doi.org/10.48550/arXiv.2306.08107
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Zoeller, M., Mauthe, F., Zeiler, P., Lindauer, M., & Huber, M. (2023). Automated Machine Learning for Remaining Useful Life Predictions. In Proceedings of the international conference on Systems Science and Engineering, Human-Machine Systems, and Cybernetics (IEEE SMC) IEEE Xplore Digital Library. https://arxiv.org/abs/2306.12215
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2022

Adriaensen, S., Biedenkapp, A., Shala, G., Awad, N., Eimer, T., Lindauer, M., & Hutter, F. (2022). Automated Dynamic Algorithm Configuration. Journal of Artificial Intelligence Research, 75, 1633-1699. https://doi.org/10.48550/arXiv.2205.13881, https://doi.org/10.1613/jair.1.13922
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