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 1 - 20 von 97

2024


Giovanelli, J., Tornede, A., Tornede, T., & Lindauer, M. (Angenommen/Im Druck). Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning. in Proceedings of the Thirty-Eighth Conference on Artificial Intelligence (AAAI'24)
Mohan, A., Zhang, A., & Lindauer, M. (Angenommen/Im Druck). Structure in Deep Reinforcement Learning: A Survey and Open Problems. Journal of Artificial Intelligence Research. https://arxiv.org/abs/2306.16021

2023


Benjamins, C., Eimer, T., Schubert, F. G., Mohan, A., Döhler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (Angenommen/Im Druck). Contextualize Me – The Case for Context in Reinforcement Learning. Transactions on Machine Learning Research. https://doi.org/10.48550/arXiv.2202.04500
Benjamins, C., Eimer, T., Schubert, F. G., Mohan, A., Döhler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (2023). Extended Abstract: Contextualize Me -- The Case for Context in Reinforcement Learning. in The 16th European Workshop on Reinforcement Learning (EWRL 2023) https://openreview.net/forum?id=DJgHzXv61b
Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Angenommen/Im Druck). Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. in AutoML Conference 2023 PMLR.
Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Angenommen/Im Druck). Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. in GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference Companion Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation (SIGEVO).
Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Thomas, J., Ullmann, T., Becker, M., Boulesteix, A-L., Deng, D., & Lindauer, M. (2023). Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(2), [e1484]. https://doi.org/10.1002/widm.1484
Denkena, B., Dittrich, M-A., Noske, H., Lange, D., Benjamins, C., & Lindauer, M. (2023). Application of machine learning for fleet-based condition monitoring of ball screw drives in machine tools. The international journal of advanced manufacturing technology, 127(3-4), 1143-1164. https://doi.org/10.1007/s00170-023-11524-9
Eimer, T., Lindauer, M., & Raileanu, R. (2023). Extended Abstract: Hyperparameters in Reinforcement Learning and How To Tune Them. in The 16th European Workshop on Reinforcement Learning (EWRL 2023) https://openreview.net/forum?id=N3IDYxLxgtW
Eimer, T., Lindauer, M., & Raileanu, R. (2023). Hyperparameters in Reinforcement Learning and How to Tune Them. in ICML'23: Proceedings of the 40th International Conference on Machine Learning (S. 9104–9149). [366] https://doi.org/10.48550/arXiv.2306.01324, https://doi.org/10.5555/3618408.3618774
Loni, M., Mohan, A., Asadi, M., & Lindauer, M. (Angenommen/Im Druck). Learning Activation Functions for Sparse Neural Networks. in Second International Conference on Automated Machine Learning PMLR. https://arxiv.org/abs/2305.10964
Mallik, N., Bergman, E., Hvarfner, C., Stoll, D., Janowski, M., Lindauer, M., Nardi, L., & Hutter, F. (2023). PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning. in Proceedings of the international Conference on Neural Information Processing Systems (NeurIPS) https://doi.org/10.48550/arXiv.2306.12370
Mohan, A., Zhang, A., & Lindauer, M. (Angenommen/Im Druck). A Patterns Framework for Incorporating Structure in Deep Reinforcement Learning. in The 16th European Workshop on Reinforcement Learning (EWRL 2023) https://openreview.net/forum?id=KkKWsPLlAx
Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A., & Lindauer, M. (Angenommen/Im Druck). AutoRL Hyperparameter Landscapes. in Second International Conference on Automated Machine Learning PMLR. https://doi.org/10.48550/arXiv.2304.02396
Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A., & Lindauer, M. (Angenommen/Im Druck). Extended Abstract: AutoRL Hyperparameter Landscapes. in The 16th European Workshop on Reinforcement Learning (EWRL 2023) https://openreview.net/forum?id=4Zu0l5lBgc
Neutatz, F., Lindauer, M., & Abedjan, Z. (2023). AutoML in Heavily Constrained Applications. VLDB Journal. https://doi.org/10.48550/arXiv.2306.16913, https://doi.org/10.1007/s00778-023-00820-1
Ruhkopf, T., Mohan, A., Deng, D., Tornede, A., Hutter, F., & Lindauer, M. (2023). MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information. Transactions on Machine Learning Research. https://openreview.net/forum?id=5aYGXxByI6
Schubert, F., Benjamins, C., Döhler, S., Rosenhahn, B., & Lindauer, M. (2023). POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. Transactions on Machine Learning Research. https://doi.org/10.48550/arXiv.2205.11357
Segel, S., Graf, H., Tornede, A., Bischl, B., & Lindauer, M. (Angenommen/Im Druck). Symbolic Explanations for Hyperparameter Optimization. in AutoML Conference 2023 PMLR. https://doi.org/10.5281/zenodo.8123425
Shoaib, M., Kotthoff, L., Lindauer, M., & Kant, S. (2023). AutoML: advanced tool for mining multivariate plant traits. Trends in Plant Science, 28(12), 1451-1452. https://doi.org/10.1016/j.tplants.2023.09.008