Marius Lindauer
Prof. Dr. rer. nat. Marius Lindauer
Appelstraße 9a
30167 Hannover
Prof. Dr. rer. nat. Marius Lindauer
Appelstraße 9a
30167 Hannover

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

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

    PostDoc, University of Freiburg

    Phd Student, University of Potsdam

  • Education

    Phd (Dr. rer. nat), University of Potsdam

    Master of Science, Computer Science, University of Potsdam

    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


Showing entries 21 - 40 out of 78
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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)

Eimer, T., Biedenkapp, A., Reimer, M., Adriaensen, S., Hutter, F., & Lindauer, M. T. (2021). DACBench: A Benchmark Library for Dynamic Algorithm Configuration. In Proceedings of the international joint conference on AI (IJCAI) (pp. 1668-1674)

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)

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)

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

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)

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].


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)

Schubert, F., Eimer, T., Rosenhahn, B., & Lindauer, M. (2021). Automatic Risk Adaptation in Distributional Reinforcement Learning.

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 (Eds.), Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Proceedings (Vol. 3, pp. 265-296). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 12977). Springer Nature Switzerland AG.

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

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)

Stürenburg, L., Denkena, B., Lindauer, M., & Wichmann, M. (2021). Maschinelles Lernen in der Prozessplanung. VDI-Z Integrierte Produktion, 163(11-12), 26-29.

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].


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.

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 (Eds.), ECAI 2020 - 24th European Conference on Artificial Intelligence (pp. 427-434). (Frontiers in Artificial Intelligence and Applications; Vol. 325).

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.

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.

Eggensperger, K., Haase, K., Müller, P., Lindauer, M., & Hutter, F. (2020). Neural Model-based Optimization with Right-Censored Observations.

Eimer, T., Biedenkapp, A., Hutter, F., & Lindauer, M. T. (2020). Towards Self-Paced Context Evaluation for Contextual Reinforcement Learning.


  • Dynamic Algorithm Configuration
    Da die Konfigurationen während der Laufzeit in Abhängigkeit vom aktuellen Zustand des Algorithmus ausgewählt werden sollten, kann es als ein Problem des Reinforcement Learning (RL) betrachtet werden, bei dem ein Agent in jedem Zeitschritt die zu verwendende Konfiguration auf der Grundlage der Leistung im letzten Schritt und des aktuellen Zustands des Algorithmus auswählt. Dies ermöglicht uns einerseits den Einsatz leistungsfähiger RL-Methoden, andererseits bringt RL auch eine Reihe von Herausforderungen mit sich, wie Instabilität, Rauschen und Ineffizienz bei der Abtastung, die bei Anwendungen wie DAC angegangen werden müssen. Daher umfasst die Forschung zu DAC auch die Forschung zu zuverlässigem, interpretierbarem, allgemeinem und schnellem Reinforcement Learning.
    Led by: Prof. Dr. Marius Lindauer
    Year: 2019
    Funding: DFG
    Duration: 2019-2023
  • CoyPu: Cognitive Economy Intelligence Platform for the Resillience of Economic Ecosystems
    Natural disasters, pandemics, financial- & political crises, supply shortages or demand shocks propagate through hidden and intermediate linkages across the global economic system. This is a consequence of the continuous international division of business and labor which is at the heart of globalisation. The aim of the project is to provide a platform that expounds the complex supply chains and reveal the linkages, compounded risks and provide companies with predictions regarding their exposure in various granularities.
    Led by: Prof. Marius Lindauer and Prof. Maria Esther-Vidal (L3S/LUH)
    Team: InfAI, DATEV eg., eccenca GmbH, Implisense GmbH, Deutsches Institut für Wirtschaftsforschung, Leibniz Informationszentrum Technik und Naturwissenschaften, Hamburger Informatik Technologie-Center e.V., Selbstregulierung Informationswirtschaft e.V., Infineo
    Year: 2021
    Funding: Innovationswettbewerb Künstliche Intelligenz (BMWK)
    Duration: 2021-2024
  • Leibniz AI Academy
    The Leibniz AI Academy aims to develop and establish a trans-curricular and interdisciplinary micro-degree program at the Leibniz Universität Hannover (LUH), in which students from different courses of study acquire competencies in the field of Artificial Intelligence
    Led by: Prof. Dr. Marius Lindauer, Prof. Dr. Ralph Ewert, Prof. Dr. Johannes Krugel
    Year: 2021
    Funding: Bundesministerium für Bildung und Forschung (BMBF)
    Duration: 2021 - 2024
    Logo of Leibniz AI academy Logo of Leibniz AI academy
  • ERC Starting Grant: Interactive and Explainable Human-Centered AutoML
    Trust and interactivity are key factors in the future development and use of automated machine learning (AutoML), supporting developers and researchers in determining powerful task-specific machine learning pipelines, including pre-processing, predictive algorithm, their hyperparameters and--if applicable--the architecture design of deep neural networks. Although AutoML is ready for its prime time after it achieved impressive results in several machine learning (ML) applications and its efficiency improved by several orders of magnitudes in recent years, democratization of machine learning via AutoML is still not achieved. In contrast to previously purely automation-centered approaches, ixAutoML is designed with human users at its heart in several stages. First of all, the foundation of trustful use of AutoML will be based on explanations of its results and processes. Therefore, we aim for: (i) Explaining static effects of design decisions in ML pipelines optimized by state-of-the-art AutoML systems. (ii) Explaining dynamic AutoML policies for temporal aspects of dynamically adapted hyperparameters while ML models are trained. These explanations will be the base for allowing interactions, bringing the best of two worlds together: human intuition and generalization capabilities for complex systems, and efficiency of systematic optimization approaches for AutoML. Concretely, we aim for: (iii) Enabling interactions between humans and AutoML by taking human's latent knowledge into account and learning when to interact. (iv) Building first ixAutoML prototypes and showing its efficiency in the context of Industry 4.0. Perfectly aligned with the EU's AI strategy and recent efforts on interpretability in the ML community, we strongly believe that this timely human-centered ixAutoML will have a substantial impact on the democratization of machine learning.
    Led by: Prof. Dr. Marius Lindauer
    Team: AutoML
    Year: 2022
    Funding: EU
    Duration: 2022-2027
  • KISSKI: AI Service Center
    The central approach for the KISSKI project is the research on AI methods and their provision with the goal of enabling a highly available AI service center for critical and sensitive infrastructures with a focus on the fields of medicine and energy. Due to their relevance to society as a whole, medicine and the energy industry are among the future fields of application-oriented AI research in Germany. Beyond the technological developments, artificial intelligence (AI) has the potential to make a significant contribution to social progress. This is particularly true in areas where digitization processes are increasingly gaining ground and complexity is high. For both medicine and the energy industry, the pressure to innovate, but also the potential, is immense due to the availability of more and more distributed information based on a multitude of new sensors and actuators. The increasing complexity of the tasks as well as the availability of very large data sets offer a high potential for the application of AI methods in both topics.
    Led by: Prof. Dr. Marius Lindauer
    Team: AutoML
    Year: 2022
    Funding: BMBF
    Duration: 2022-2025
  • Fair Benchmarking for Dynamic Algorithm Configuration
    Dynamic Algorithm Configuration (DAC) aims at dynamically adjusting the hyperparameters of a target algorithm to improve performance in a data-driven manner [Biedenkapp et al., 2020]. This allows to not only statically tune hyperparameters, but adjust them while the learning takes place. In contrast to previous methods, this allows us to deeply go into the algorithms and open up new potentials to further improvements of performance. Theoretical and empirical results demonstrated the advantages of dynamically controlling hyperparameters, e.g. in the domains of deep learning, evolutionary algorithms and AI planning [Daniel et al., 2016; Vermetten et al., 2019; Doerr & Doerr, 2020; Shala et al. 2020; Speck et al., 2021]. However, there are several remaining challenges and opportunities to improve DAC w.r.t. building trust into DAC by considering the various aspects of trustworthy AI (transparency, explainability, fairness, robustness, and privacy) as defined in the EC Guidelines for Trustworthy AI and the Assessment List for Trustworthy AI. These include: (i) Typically AI models are overfitted to the selected data instances for training, which means that their selected hyperparameters can not generalize the learned knowledge on new data instances that were not involved in the training process. (ii) There is an inherent bias presented in the learning process that originates from the quality of data used in the training process. (iii) There are no quantitative measures to estimate the level of trust/confidence when using a developed AI model on new unseen data, which will indicate to which level generalization of the learned knowledge is possible. To overcome the aforementioned challenges, the goals of our project are: 1. To investigate different meta-feature representations for data instances for DAC learning tasks presented in the DACBench library, which will allow us to perform complementary analysis between them and perform a landscape coverage analysis of the problem/feature space, leading to a thorough and fair comparison of DAC methods. The utility of meta-feature representations will also be investigated by transforming them with matrix factorization and deep learning techniques. 2. To develop methodologies that will allow us automatically to select more representative data instances that will uniformly cover the landscape space of the data instances using their meta-feature representation, which can be used for benchmarking studies to produce reproducible and transferable results. 3. To define quantitative indicators that will measure the level of diversity between the data instances used for training DAC and instances used for testing by using their meta-feature representation. This will provide researchers the level of trust of applying the selected hyperparameters on new unseen data instances. 4. To determine computational time and energy variables that will be measured to estimate the greener level (i.e. encouraging a reduction in resources spent) of performing DAC experiments only on the selected representative data instances.
    Led by: Prof. Dr. Marius Lindauer
    Year: 2023
    Funding: DAAD
    Duration: 2023-2024
  • GreenAutoML4FAS - Automated Green-ML for Driver Assistance Systems
    Led by: Prof. Dr. Marius Lindauer
    Team: AutoML
    Year: 2023
    Funding: BMUV
    Duration: 2023 - 2026