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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.lindauerai.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.lindauerai.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

First 1 2 3 4 5 6 7 8 Last

2025

Mladenovic, S., Lindauer, M., & Doerr, C. (2025). Automated Data Preparation for Machine Learning. in 4th International Conference on Automated Machine Learning: Non-Archival Track Vorzeitige Online-Publikation. https://openreview.net/forum?id=qjLJgQNipN
Mehr...
Mohan, A., Eimer, T., Benjamins, C., Lindauer, M., & Biedenkapp, A. (2025). Mighty: A Comprehensive Tool for studying Generalization, Meta-RL and AutoRL. in 18th European Workshop on Reinforcement Learning (EWRL) Vorzeitige Online-Publikation. https://openreview.net/pdf?id=QlDXH5NkUx
Mehr...
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
Mehr...
Schaller, M. C., Kruse, M., Ortega, A., Lindauer, M., & Rosenhahn, B. (2025). 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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Segel, S., Graf, H., Bergman, E., Thieme, K., Wever, M. D., Tornede, A., Hutter, F., & Lindauer, M. (Angenommen/Im Druck). DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning. Journal of Machine Learning Research, 2025(26). http://jmlr.org/papers/v26/24-1353.html
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Zöller, M., Lindauer, M., & Huber, M. (2025). auto-sktime: Automated Time Series Forecasting. in P. Festa, D. Ferone, T. Pastore, & O. Pisacane (Hrsg.), Proceedings of the 18TH Learning and Intelligent Optimization Conference (LION) (S. 456–471). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 14990 LNCS). https://doi.org/10.1007/978-3-031-75623-8_35, https://doi.org/10.48550/arXiv.2312.08528
Mehr...

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) Vorzeitige Online-Publikation. https://doi.org/10.48550/arXiv.2409.18827
Mehr...
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.
Mehr...
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 Vorzeitige Online-Publikation.
Mehr...
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) (S. 563 - 566). Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation (SIGEVO). https://doi.org/10.1145/3638530.3654291
Mehr...
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
Mehr...
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
Mehr...
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
Mehr...
Hennig, L., Tornede, T., & Lindauer, M. (2024). Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks. Vorzeitige Online-Publikation. https://doi.org/10.48550/arXiv.2404.01965
Mehr...
Lindauer, M., Karl, F., Klier, A., Moosbauer, J., Tornede, A., Müller, A., Hutter, F., Feurer, M., & Bischl, B. (2024). A Call to Action for a Human-Centered AutoML Paradigm. in Proceedings of the international conference on machine learning (S. 30566 - 30584). Artikel 1231 https://dl.acm.org/doi/10.5555/3692070.3693301
Mehr...
Mohan, A., Zhang, A., & Lindauer, M. (2024). Structure in Deep Reinforcement Learning: A Survey and Open Problems. Journal of Artificial Intelligence Research, 79, 1167-1236. https://doi.org/10.1613/jair.1.15703
Mehr...
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)
Mehr...
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
Mehr...
Theodorakopoulos, D., Stahl, F., & Lindauer, M. (2024). Hyperparameter Importance Analysis for Multi-Objective AutoML. in U. Endriss, F. S. Melo, K. Bach, A. Bugarin-Diz, J. M. Alonso-Moral, S. Barro, & F. Heintz (Hrsg.), Proceedings of the european conference on AI (ECAI) (S. 1100-1107). (Frontiers in Artificial Intelligence and Applications; Band 392). https://doi.org/10.3233/FAIA240602, https://doi.org/10.48550/arXiv.2405.07640
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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. (2024). AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. Transactions on Machine Learning Research. Vorzeitige Online-Publikation. https://doi.org/10.48550/arXiv.2306.08107
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Letzte Änderung: 25.06.25 Druckversion

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