Leibniz Universität Hannover Fakultät Fakultät für Elektrotechnik und Informatik
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Institut für Künstliche Intelligenz
 
Institut für Künstliche Intelligenz LUHAI Institut
Marius Lindauer
Porträt von Prof. Dr. rer. nat. Marius Lindauer mit Brille, weißem Hemd und Anhänger-Halskette, lächelnd vor einem hellgrauen Hintergrund. Porträt von Prof. Dr. rer. nat. Marius Lindauer mit Brille, weißem Hemd und Anhänger-Halskette, lächelnd vor einem hellgrauen Hintergrund. © M4 Fotostudio Mirja John
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
Porträt von Prof. Dr. rer. nat. Marius Lindauer mit Brille, weißem Hemd und Anhänger-Halskette, lächelnd vor einem hellgrauen Hintergrund. Porträt von Prof. Dr. rer. nat. Marius Lindauer mit Brille, weißem Hemd und Anhänger-Halskette, lächelnd vor einem hellgrauen Hintergrund. © M4 Fotostudio Mirja John
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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2025

Fehring, L., Eimer, T., & Lindauer, M. (Angenommen/im Druck). Growing with Experience: Growing Neural Networks in Deep Reinforcement Learning. In 2025 Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2025)
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Jabs, D., Mohan, A., & Lindauer, M. (Angenommen/im Druck). Moments Matter: Stabilizing Policy Optimization using Return Distributions. In 2025 Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2025)
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Margraf, V., Lappe, A., Wever, M. D., Benjamins, C., Hüllermeier, E., & Lindauer, M. (2025). SynthACticBench: A Capability-Based Synthetic Benchmark for Algorithm Configuration. In GECCO 2025 - Proceedings of the 2025 Genetic and Evolutionary Computation Conference (ACM Conferences). Association for Computing Machinery (ACM). Vorabveröffentlichung online.
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Neutatz, F., Lindauer, M., & Abedjan, Z. (2025). How Green is AutoML for Tabular Data? In Proceedings 28th International Conference on Extending Database Technology ( EDBT 2025 ) (S. 350–363) https://openproceedings.org/2025/conf/edbt/paper-97.pdf
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Rook, J., Benjamins, C., Bossek, J., Trautmann, H., Hoos, H., & Lindauer, M. (2025). MO-SMAC: Multi-objective Sequential Model-based Algorithm Configuration. Evolutionary computation, 25(1), 1-25. https://doi.org/10.1162/evco_a_00371
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Schaller, M. C., Kruse, M., Ortega, A., Lindauer, M., & Rosenhahn, B. (Angenommen/im Druck). Automl for Multi-Class Anomaly Compensation of Sensor Drift. Measurement: Journal of the International Measurement Confederation, 250, Artikel 117097. https://doi.org/10.1016/j.measurement.2025.117097
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2024

Becktepe, J., Dierkes, J., Benjamins, C., Mohan, A., Salinas, D., Rajan, R., Hutter, F., Hoos, H., Lindauer, M., & Eimer, T. (2024). ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. In 17th European Workshop on Reinforcement Learning (EWRL 2024) Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2409.18827
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Benjamins, C., Surana, S., Bent, O., Lindauer, M., & Duckworth, P. (2024). Bayesian Optimisation for Protein Sequence Design: Gaussian Processes with Zero-Shot Protein Language Model Prior Mean. Beitrag in The 38th Annual Conference on Neural Information Processing Systems, Vancouver, Kanada.
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Benjamins, C., Surana, S., Bent, O., Lindauer, M., & Duckworth, P. (2024). Bayesian Optimization for Protein Sequence Design: Back to Simplicity with Gaussian Processes. In AI for Accelerated Materials Design - NeurIPS Workshop 2024 Vorabveröffentlichung online.
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Benjamins, C., Cenikj, G., Nikolikj, A., Mohan, A., Eftimov, T., & Lindauer, M. (2024). Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization. In Genetic and Evolutionary Computation Conference (GECCO) Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation (SIGEVO). Vorabveröffentlichung online.
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Bergman, E., Feurer, M., Bahram, A., Rezaei, A., Purucker, L., Segel, S., Lindauer, M., & Eggensperger, K. (2024). AMLTK: A Modular AutoML Toolkit in Python. The Journal of Open Source Software, 9(100), Artikel 6367. https://doi.org/10.21105/joss.06367
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Deng, D., & Lindauer, M. (2024). Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach. In NeurIPS Workshop on Time Series in the Age of Large Models (ArXiv). Vorabveröffentlichung online. https://arxiv.org/abs/2406.05088
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Eimer, T., Hutter, F., Lindauer, M., & Biedenkapp, A. (2024). Verfahren zum Trainieren eines Algorithmus des maschinellen Lernens durch ein bestärkendes Lernverfahren. (Patent Nr. DE102022210480A1). Deutsches Patent- und Markenamt (DPMA). https://worldwide.espacenet.com/patent/search/family/090246319/publication/DE102022210480A1?q=pn%3DDE102022210480A1
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Giovanelli, J., Tornede, A., Tornede, T., & Lindauer, M. (2024). Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning. In M. Wooldridge, J. Dy, & S. Natarajan (Hrsg.), Proceedings of the 38th conference on AAAI (S. 12172-12180). (Proceedings of the AAAI Conference on Artificial Intelligence; Band 38, Nr. 11). https://doi.org/10.48550/arXiv.2309.03581, https://doi.org/10.1609/aaai.v38i11.29106
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Hennig, L., Tornede, T., & Lindauer, M. (2024). Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks. In 5th Workshop on practical ML for limited/low resource settings Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2404.01965
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Lindauer, M., Karl, F., Klier, A., Moosbauer, J., Tornede, A., Müller, A., Hutter, F., Feurer, M., & Bischl, B. (2024). Position Paper: A Call to Action for a Human-Centered AutoML Paradigm. In Proceedings of the international conference on machine learning Vorabveröffentlichung online.
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Mohan, A., Zhang, A., & Lindauer, M. (2024). Structure in Deep Reinforcement Learning: A Survey and Open Problems. Journal of Artificial Intelligence Research. Vorabveröffentlichung online. https://arxiv.org/abs/2306.16021
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Mohan, A., & Lindauer, M. (Angenommen/im Druck). Towards Enhancing Predictive Representations using Relational Structure in Reinforcement Learning. In The 17th European Workshop on Reinforcement Learning (EWRL 2024)
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Neutatz, F., Lindauer, M., & Abedjan, Z. (2024). AutoML in Heavily Constrained Applications. VLDB Journal, 33(4), 957–979. https://doi.org/10.48550/arXiv.2306.16913, https://doi.org/10.1007/s00778-023-00820-1
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Theodorakopoulos, D., Stahl, F., & Lindauer, M. (Angenommen/im Druck). Hyperparameter Importance Analysis for Multi-Objective AutoML. In Proceedings of the european conference on AI (ECAI)
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Letzte Änderung: 30.01.25 Druckversion

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