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 61 - 80 out of 109

2020

Biedenkapp, A., Rajan, R., Hutter, F., & Lindauer, M. T. (2020). Towards TempoRL Learning When to Act. Paper presented at 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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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
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Eggensperger, K., Haase, K., Müller, P., Lindauer, M., & Hutter, F. (2020). Neural Model-based Optimization with Right-Censored Observations. Advance online publication. https://arxiv.org/abs/2009.13828
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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
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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 (Eds.), Parallel Problem Solving from Nature – PPSN XVI: 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I (pp. 691-706). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12269). Springer. Advance online publication. https://doi.org/10.1007/978-3-030-58112-1_48
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2019

Biedenkapp, A., Bozkurt, H. F., Hutter, F., & Lindauer, M. (2019). Towards White-box Benchmarks for Algorithm Control. Advance online publication. https://arxiv.org/abs/1906.07644
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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
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Fuks, L., Awad, N., Hutter, F., & Lindauer, M. (2019). An evolution strategy with progressive episode lengths for playing games. In S. Kraus (Ed.), Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 (pp. 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
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Lindauer, M. T. (2019). Automated Algorithm Selection –Predict which algorithm to use!. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjd94W5k9TqAhWM_KQKHVbABogQFjAAegQIARAB&url=http%3A%2F%2Fceur-ws.org%2FVol-2360%2Fpaper2Keynote.pdf&usg=AOvVaw0h4cvTGwQg-XD97fhGaytv
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Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Marben, J., Müller, P., & Hutter, F. (2019). BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters. Advance online publication. https://arxiv.org/pdf/1908.06756
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Lindauer, M. T. (2019). Hands-On Automated Machine Learning Tools: Auto-Sklearn and Auto-PyTorch. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwibqKyPlNTqAhUwMewKHR7PC0oQFjAAegQIARAB&url=https%3A%2F%2Fwww.automl.org%2Fevents%2Famir19-key-note-and-automl-hands-on%2F&usg=AOvVaw1ggAEDpu7zdnlhnRtLdGQD
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Lindauer, M., van Rijn, J. N., & Kotthoff, L. (2019). The algorithm selection competitions 2015 and 2017. Artificial intelligence, 272, 86-100. Advance online publication. https://doi.org/10.1016/j.artint.2018.10.004
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Lindauer, M., Feurer, M., Eggensperger, K., Biedenkapp, A., & Hutter, F. (2019). Towards Assessing the Impact of Bayesian Optimization’s Own Hyperparameters. In DSO Workshop at IJCAI Advance online publication. https://arxiv.org/abs/1908.06674
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Mendoza, H., Klein, A., Feurer, M., Springenberg, J. T., Urban, M., Burkart, M., Dippel, M., Lindauer, M. T., & Hutter, F. (2019). Towards Automatically-Tuned Deep Neural Networks. In Automated Machine Learning https://doi.org/10.1007/978-3-030-05318-5_7
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2018

Biedenkapp, A., Marben, J., Lindauer, M., & Hutter, F. (2018). CAVE: Configuration Assessment, Visualization and Evaluation. In P. M. Pardalos, R. Battiti, M. Brunato, & I. Kotsireas (Eds.), Learning and Intelligent Optimization (pp. 115-130). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11353 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-05348-2_10
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Eggensperger, K., Lindauer, M., Hoos, H. H., Hutter, F., & Leyton-Brown, K. (2018). Efficient benchmarking of algorithm configurators via model-based surrogates. Machine learning, 107(1), 15-41. Advance online publication. https://doi.org/10.1007/s10994-017-5683-z
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Eggensperger, K., Lindauer, M., & Hutter, F. (2018). Neural Networks for Predicting Algorithm Runtime Distributions. In J. Lang (Ed.), Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (pp. 1442-1448). AAAI Press/International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2018/200
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Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M. T., & Hutter, F. (2018). Practical Automated Machine Learning for the AutoML Challenge 2018. https://www.tnt.uni-hannover.de/papers/data/1407/18-AUTOML-AutoChallenge.pdf
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Lindauer, M., Hoos, H., Hutter, F., & Leyton-Brown, K. (2018). Selection and Configuration of Parallel Portfolios. In Handbook of Parallel Constraint Reasoning (pp. 583-615). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-63516-3_15
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Lindauer, M. T., van Rijn, J. N., & Kotthoff, L. (2018). The Algorithm Selection Competition Series 2015-17. Advance online publication. https://arxiv.org/abs/1805.01214v1
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Last Change: 22.04.24 Print

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