InstituteStaff
Marius Lindauer
Prof. Dr. rer. nat. Marius Lindauer
Address
Appelstraße 9a
30167 Hannover
Building
Room
Prof. Dr. rer. nat. Marius Lindauer
Address
Appelstraße 9a
30167 Hannover
Building
Room

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

  • Working Experience

    since 2022
    Head of Institute of AI, Leibniz University Hannover

    since 2019
    Professor of Machine Learning, Leibniz University Hannover

    2017-2019
    Lecturer (i.e., "Akademischer Rat"), University of Freiburg

    2014-2017
    PostDoc, University of Freiburg

    2010-2014
    Phd Student, University of Potsdam

  • Education

    2010-2015
    Phd (Dr. rer. nat), University of Potsdam

    2008-2010
    Master of Science, Computer Science, University of Potsdam

    2005-2008
    Bachelor of Science, Computer Science, University of Potsdam

  • Selected Awards
    • 2022: ERC Starting Grant on ixAutoML
    • 2020: 3rd place(*) at the official leaderboard and 1st place at the warmstart friendly leaderboard at the BBO-Challenge at NeurIPS'20 (* after fixing a minor bug)
    • 2018: Winner of 2nd AutoML challenge::PAKDD2018 with aad_freibug and PoSH Auto-sklearn
    • 2016: Winner of ChaLearn AutoML challenge "AutoML 5" with aad_freibug and auto-sklearn
    • 2015: Winner of ICON Challenge on algorithm selection with AutoFolio (track: Par10)
    • 2013: Winner of Configurable SAT Solver challenge 2013 with the Potassco team and clasp (tracks: crafted and random)
    • 2012: Winner of SAT Challenge 2012 with the Potassco team and clasp (track: hard combinatorial)
    • 2011: Winner of Answer Set Programming Competition with the Potassco team and claspfolio (track: NP-Problems)
    • 2009: Leopold-von-Buch-Bachelor-Award (Best Bachelor in Natural Sciences 2009 at the University of Potsdam)
  • Memberships
  • Social Media

Publications


Showing entries 1 - 20 out of 87
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2023


Benjamins, C., Eimer, T., Schubert, F. G., Mohan, A., Döhler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (Accepted/In press). Contextualize Me – The Case for Context in Reinforcement Learning. Transactions on Machine Learning Research.

Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Accepted/In press). Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. In AutoML Conference 2023 PMLR.

Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Accepted/In press). 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].

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.

doi.org/10.1007/s00170-023-11524-9

Eimer, T., Lindauer, M., & Raileanu, R. (Accepted/In press). Hyperparameters in Reinforcement Learning and How To Tune Them. In Proceeding of the Fortieth International Conference on Machine Learning (Proceeding of the International Conference on Machine Learning).

Loni, M., Mohan, A., Asadi, M., & Lindauer, M. (Accepted/In press). Learning Activation Functions for Sparse Neural Networks. In Second International Conference on Automated Machine Learning PMLR.

arxiv.org/abs/2305.10964

Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A., & Lindauer, M. (Accepted/In press). AutoRL Hyperparameter Landscapes. In Second International Conference on Automated Machine Learning PMLR.

doi.org/10.48550/arXiv.2304.02396

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.

openreview.net/forum

Segel, S., Graf, H., Tornede, A., Bischl, B., & Lindauer, M. (Accepted/In press). Symbolic Explanations for Hyperparameter Optimization. In AutoML Conference 2023 PMLR.

Theodorakopoulos, D., Manß, C., Stahl, F., & Lindauer, M. (2023). Green-AutoML for Plastic Litter Detection. In Proceedings of the ICLR Workshop on Tackling Climate Change with Machine Learning

www.climatechange.ai/papers/iclr2023/53

Zöller, M., Mauthe, F., Zeiler, P., Lindauer, M., & Huber, M. (2023). Automated Machine Learning for Remaining Useful Life Predictions. In Proceedings of the international conference on Systems Science and Engineering, Human-Machine Systems, and Cybernetics (IEEE SMC) IEEE Xplore Digital Library.


2022


Adriaensen, S., Biedenkapp, A., Shala, G., Awad, N., Eimer, T., Lindauer, M., & Hutter, F. (2022). Automated Dynamic Algorithm Configuration. Journal of Artificial Intelligence Research, 75.

doi.org/10.48550/arXiv.2205.13881

,

doi.org/10.1613/jair.1.13922

Benjamins, C., Raponi, E., Jankovic, A., Blom, K. V. D., Santoni, M. L., Lindauer, M., & Doerr, C. (2022). PI is back! Switching Acquisition Functions in Bayesian Optimization. In 2022 NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems

arxiv.org/abs/2211.01455

Benjamins, C., Jankovic, A., Raponi, E., Blom, K. V. D., Lindauer, M., & Doerr, C. (2022). Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis. In 6th Workshop on Meta-Learning at NeurIPS 2022

Bothmann, L., Strickroth, S., Casalicchio, G., Rügamer, D., Lindauer, M., Scheipl, F., & Bischl, B. (2022). Developing Open Source Educational Resources for Machine Learning and Data Science. In Teaching Machine Learning Workshop at ECML 2022

arxiv.org/abs/2107.14330

Deng, D., Karl, F., Hutter, F., Bischl, B., & Lindauer, M. (2022). Efficient Automated Deep Learning for Time Series Forecasting. In Proceedings of the European Conference on Machine Learning (ECML)

doi.org/10.48550/arXiv.2205.05511

Deng, D., & Lindauer, M. (2022). Searching in the Forest for Local Bayesian Optimization. In ECML/PKDD workshop on Meta-learning

arxiv.org/abs/2111.05834

Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M. T., & Hutter, F. (2022). Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning. Journal of Machine Learning Research, 23.

www.jmlr.org/papers/volume23/21-0992/21-0992.pdf

Hvarfner, C., Stoll, D., Souza, A. L. F., Lindauer, M., Hutter, F., & Nardi, L. (Accepted/In press). π BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. In Proceedings of the International conference on Learning Representation (ICLR)

doi.org/10.48550/arXiv.2204.11051