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
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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Zeige Ergebnisse 41 - 60 von 108

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 Vorabveröffentlichung online. 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) Vorabveröffentlichung online. 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) Vorabveröffentlichung online. 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 (Hrsg.), Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21) (S. 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) Vorabveröffentlichung online. 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 Vorabveröffentlichung online. 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 Nr. 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) Vorabveröffentlichung online. 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. Artikel 9415128. Vorabveröffentlichung online. 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) Vorabveröffentlichung online. 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. Vorabveröffentlichung online. 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 (Hrsg.), Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Proceedings (Band 3, S. 265-296). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Band 12977). Springer Nature Switzerland AG. Vorabveröffentlichung online. 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 Vorabveröffentlichung online. 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) Vorabveröffentlichung online. 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. Artikel 9382913. Vorabveröffentlichung online. 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. Vorabveröffentlichung online. 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 (Hrsg.), ECAI 2020 - 24th European Conference on Artificial Intelligence (S. 427-434). (Frontiers in Artificial Intelligence and Applications; Band 325). https://doi.org/10.3233/FAIA200122
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Biedenkapp, A., Rajan, R., Hutter, F., & Lindauer, M. T. (2020). Towards TempoRL Learning When to Act. Beitrag in ICML 2020 Inductive biases, invariances and generalization in RL workshop. https://www.tnt.uni-hannover.de/papers/data/1455/20-BIG-TempoRL.pdf
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