Leibniz Universität Hannover Fakultät Fakultät für Elektrotechnik und Informatik
Kontakt
Institut für Künstliche Intelligenz
 
InstitutTeam
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

Zeige Ergebnisse 41 - 60 von 93
First 1 2 3 4 5 Last

2021

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) https://arxiv.org/abs/2106.11189
Mehr...
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. [9415128]. https://doi.org/10.48550/arXiv.2201.03801, https://doi.org/10.1109/TPAMI.2021.3075372
Mehr...
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) https://arxiv.org/abs/2111.04820
Mehr...
Schubert, F., Eimer, T., Rosenhahn, B., & Lindauer, M. (2021). Automatic Risk Adaptation in Distributional Reinforcement Learning. https://arxiv.org/abs/2106.06317
Mehr...
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. https://doi.org/10.1007/978-3-030-86523-8_17
Mehr...
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 https://arxiv.org/pdf/2006.14608
Mehr...
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) https://arxiv.org/abs/2006.08246
Mehr...
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
Mehr...
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. [9382913]. https://doi.org/10.1109/TPAMI.2021.3067763
Mehr...

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. https://arxiv.org/abs/2012.08180
Mehr...
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
Mehr...
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
Mehr...
Denkena, B., Dittrich, M-A., Lindauer, M. T., Mainka, J. M., & Stürenburg, L. K. (2020). Using AutoML to Optimize Shape Error Prediction in Milling Processes. SSRN Electronic Journal, 2020. https://doi.org/10.2139/ssrn.3724234
Mehr...
Eggensperger, K., Haase, K., Müller, P., Lindauer, M., & Hutter, F. (2020). Neural Model-based Optimization with Right-Censored Observations. https://arxiv.org/abs/2009.13828
Mehr...
Eimer, T., Biedenkapp, A., Hutter, F., & Lindauer, M. T. (2020). Towards Self-Paced Context Evaluation for Contextual Reinforcement Learning. https://www.tnt.uni-hannover.de/papers/data/1454/space.pdf
Mehr...
Lindauer, M., & Hutter, F. (2020). Best Practices for Scientific Research on Neural Architecture Search. Journal of Machine Learning Research, 21. https://arxiv.org/abs/1909.02453
Mehr...
Shala, G., Biedenkapp, A., Awad, N., Adriaensen, S., Lindauer, M., & Hutter, F. (2020). Learning Step-Size Adaptation in CMA-ES. in T. Bäck, M. Preuss, A. Deutz, M. Emmerich, H. Wang, C. Doerr, & H. Trautmann (Hrsg.), Parallel Problem Solving from Nature – PPSN XVI: 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I (S. 691-706). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12269). Springer. https://doi.org/10.1007/978-3-030-58112-1_48
Mehr...

2019

Biedenkapp, A., Bozkurt, H. F., Hutter, F., & Lindauer, M. (2019). Towards White-box Benchmarks for Algorithm Control. https://arxiv.org/abs/1906.07644
Mehr...
Eggensperger, K., Lindauer, M., & Hutter, F. (2019). Pitfalls and Best Practices in Algorithm Configuration. Journal of Artificial Intelligence Research, 64, 861-893. https://doi.org/10.1613/jair.1.11420
Mehr...
Fuks, L., Awad, N., Hutter, F., & Lindauer, M. (2019). An evolution strategy with progressive episode lengths for playing games. in S. Kraus (Hrsg.), Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 (S. 1234-1240). (IJCAI International Joint Conference on Artificial Intelligence). AAAI Press/International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2019/172
Mehr...

Letzte Änderung: 19.09.23 Druckversion

Gefördert vom

So erreichen Sie uns
Kontakt