Institut
Marius Lindauer
Prof. Dr. rer. nat. Marius Lindauer
Adresse
Welfengarten 1
30167 Hannover
Gebäude
Raum
Prof. Dr. rer. nat. Marius Lindauer
Adresse
Welfengarten 1
30167 Hannover
Gebäude
Raum

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 97

2021


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) https://arxiv.org/abs/2109.06716
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
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
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) https://www.tnt.uni-hannover.de/papers/data/1454/space.pdf
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 https://arxiv.org/abs/2105.01015
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
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
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
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
Schubert, F., Eimer, T., Rosenhahn, B., & Lindauer, M. (2021). Automatic Risk Adaptation in Distributional Reinforcement Learning. https://arxiv.org/abs/2106.06317
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
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
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://doi.org/10.1609/icaps.v31i1.16008
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
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

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
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
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
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
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