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
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

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Showing results 41 - 60 out of 109

2022

Parker-Holder, J., Rajan, R., Song, X., Biedenkapp, A., Miao, Y., Eimer, T., Zhang, B., Nguyen, V., Calandra, R., Faust, A., Hutter, F., & Lindauer, M. (2022). Automated Reinforcement Learning (AutoRL): A Survey and Open Problems. Journal of Artificial Intelligence Research, 74(74), 517-568. https://doi.org/10.48550/arXiv.2201.03916, https://doi.org/10.1613/jair.1.13596
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2021

Benjamins, C., Eimer, T., Schubert, F., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (2021). CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. In Workshop on Ecological Theory of Reinforcement Learning, NeurIPS 2021 Advance online publication. https://arxiv.org/abs/2110.02102
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Biedenkapp, A., Rajan, R., Hutter, F., & Lindauer, M. (2021). TempoRL: Learning When to Act. In Proceedings of the international conference on machine learning (ICML) Advance online publication. https://arxiv.org/abs/2106.05262
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Eggensperger, K., Müller, P., Mallik, N., Feurer, M., Sass, R., Awad, N., Lindauer, M., & Hutter, F. (2021). HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. In Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track) Advance online publication. https://arxiv.org/abs/2109.06716
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Eimer, T., Biedenkapp, A., Reimer, M., Adriaensen, S., Hutter, F., & Lindauer, M. T. (2021). DACBench: A Benchmark Library for Dynamic Algorithm Configuration. In Z-H. Zhou (Ed.), Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21) (pp. 1668-1674). (IJCAI International Joint Conference on Artificial Intelligence). https://doi.org/10.24963/ijcai.2021/230
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Eimer, T., Benjamins, C., & Lindauer, M. T. (2021). Hyperparameters in Contextual RL are Highly Situational. In International Workshop on Ecological Theory of RL (at NeurIPS) https://doi.org/10.48550/arXiv.2212.10876
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Eimer, T., Biedenkapp, A., Hutter, F., & Lindauer, M. (2021). Self-Paced Context Evaluation for Contextual Reinforcement Learning. In Proceedings of the international conference on machine learning (ICML) Advance online publication. https://www.tnt.uni-hannover.de/papers/data/1454/space.pdf
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Guerrero-Viu, J., Hauns, S., Izquierdo, S., Miotto, G., Schrodi, S., Biedenkapp, A., Elsken, T., Deng, D., Lindauer, M., & Hutter, F. (2021). Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization. In ICML 2021 Workshop AutoML Advance online publication. https://arxiv.org/abs/2105.01015
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Hutter, F., Fuks, L., Lindauer, M., & Awad, N. (2021). Verfahren, Vorrichtung und Computerprogramm zum Erstellen einer Strategie für einen Roboter. (Patent No. DE102019210372A1). Deutsches Patent- und Markenamt (DPMA). https://worldwide.espacenet.com/patent/search?q=pn%3DCN112215363A
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Kadra, A., Lindauer, M., Hutter, F., & Grabocka, J. (2021). Well-tuned Simple Nets Excel on Tabular Datasets. In Proceedings of the international conference on Advances in Neural Information Processing Systems (NeurIPS 2021) Advance online publication. https://arxiv.org/abs/2106.11189
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Liu, Z., Pavao, A., Xu, Z., Escalera, S., Ferreira, F., Guyon, I., Hong, S., Hutter, F., Ji, R., Junior, J. C. S. J., Li, G., Lindauer, M., Luo, Z., Madadi, M., Nierhoff, T., Niu, K., Pan, C., Stoll, D., Treguer, S., ... Zhang, Y. (2021). Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3108-3125. Article 9415128. Advance online publication. https://doi.org/10.48550/arXiv.2201.03801, https://doi.org/10.1109/TPAMI.2021.3075372
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Moosbauer, J., Herbinger, J., Casalicchio, G., Lindauer, M., & Bischl, B. (2021). Explaining Hyperparameter Optimization via Partial Dependence Plots. In Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) Advance online publication. https://arxiv.org/abs/2111.04820
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Schubert, F., Eimer, T., Rosenhahn, B., & Lindauer, M. (2021). Automatic Risk Adaptation in Distributional Reinforcement Learning. Advance online publication. https://arxiv.org/abs/2106.06317
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Souza, A., Nardi, L., Oliveira, L. B., Olukotun, K., Lindauer, M., & Hutter, F. (2021). Bayesian Optimization with a Prior for the Optimum. In N. Oliver, F. Pérez-Cruz, S. Kramer, J. Read, & J. A. Lozano (Eds.), Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Proceedings (Vol. 3, pp. 265-296). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 12977). Springer Nature Switzerland AG. Advance online publication. https://doi.org/10.1007/978-3-030-86523-8_17
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Souza, A., Nardi, L., Oliveira, L. B., Olukotun, K., Lindauer, M., & Hutter, F. (2021). Prior-guided Bayesian Optimization. In Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021 Advance online publication. https://arxiv.org/pdf/2006.14608
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Speck, D., Biedenkapp, A., Hutter, F., Mattmüller, R., & Lindauer, M. (2021). Learning Heuristic Selection with Dynamic Algorithm Configuration. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS) Advance online publication. https://doi.org/10.1609/icaps.v31i1.16008
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Stürenburg, L., Denkena, B., Lindauer, M., & Wichmann, M. (2021). Maschinelles Lernen in der Prozessplanung. VDI-Z Integrierte Produktion, 163(11-12), 26-29. https://doi.org/10.37544/0042-1766-2021-11-12-26
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Zimmer, L., Lindauer, M., & Hutter, F. (2021). Auto-PyTorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3079-3090. Article 9382913. Advance online publication. https://doi.org/10.1109/TPAMI.2021.3067763
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2020

Awad, N., Shala, G., Deng, D., Mallik, N., Feurer, M., Eggensperger, K., Biedenkapp, A., Vermetten, D., Wang, H., Doerr, C., Lindauer, M., & Hutter, F. (2020). Squirrel: A Switching Hyperparameter Optimizer. Advance online publication. https://arxiv.org/abs/2012.08180
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Biedenkapp, A., Bozkurt, H. F., Eimer, T., Hutter, F., & Lindauer, M. T. (2020). Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework. In G. De Giacomo, A. Catala, B. Dilkina, M. Milano, S. Barro, A. Bugarin, & J. Lang (Eds.), ECAI 2020 - 24th European Conference on Artificial Intelligence (pp. 427-434). (Frontiers in Artificial Intelligence and Applications; Vol. 325). https://doi.org/10.3233/FAIA200122
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