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Prof. Dr.  Marius Thomas Lindauer

https://orcid.org/0000-0002-9675-3175
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Publications

  1. 2025
  2. E-pub ahead of print

    SynthACticBench: A Capability-Based Synthetic Benchmark for Algorithm Configuration

    Margraf, V., Lappe, A., Wever, M. D., Benjamins, C., Hüllermeier, E. & Lindauer, M., Jun 2025, (E-pub ahead of print) GECCO 2025 - Proceedings of the 2025 Genetic and Evolutionary Computation Conference . Association for Computing Machinery (ACM), (ACM Conferences).

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  3. Published

    MO-SMAC: Multi-objective Sequential Model-based Algorithm Configuration

    Rook, J., Benjamins, C., Bossek, J., Trautmann, H., Hoos, H. & Lindauer, M., 10 Mar 2025, In: Evolutionary computation. 25, 1, p. 1-25

    Research output: Contribution to journal › Article › Research › peer review

  4. Accepted/In press

    Growing with Experience: Growing Neural Networks in Deep Reinforcement Learning

    Fehring, L., Eimer, T. & Lindauer, M., 15 Feb 2025, (Accepted/In press) 2025 Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2025).

    Research output: Chapter in book/report/conference proceeding › Conference abstract › Research › peer review

  5. Accepted/In press

    Moments Matter: Stabilizing Policy Optimization using Return Distributions

    Jabs, D., Mohan, A. & Lindauer, M., 15 Feb 2025, (Accepted/In press) 2025 Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2025).

    Research output: Chapter in book/report/conference proceeding › Conference abstract › Research › peer review

  6. Accepted/In press

    Automl for Multi-Class Anomaly Compensation of Sensor Drift

    Schaller, M. C., Kruse, M., Ortega, A., Lindauer, M. & Rosenhahn, B., 15 Jun 2025, In: Measurement: Journal of the International Measurement Confederation. 250, 117097.

    Research output: Contribution to journal › Article › Research › peer review

  7. Published

    How Green is AutoML for Tabular Data?

    Neutatz, F., Lindauer, M. & Abedjan, Z., 2025, Proceedings 28th International Conference on Extending Database Technology ( EDBT 2025 ). p. 350–363

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  8. 2024
  9. E-pub ahead of print

    Bayesian Optimization for Protein Sequence Design: Back to Simplicity with Gaussian Processes

    Benjamins, C., Surana, S., Bent, O., Lindauer, M. & Duckworth, P., Dec 2024, (E-pub ahead of print) AI for Accelerated Materials Design - NeurIPS Workshop 2024.

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  10. Accepted/In press

    Towards Enhancing Predictive Representations using Relational Structure in Reinforcement Learning

    Mohan, A. & Lindauer, M., 30 Sept 2024, (Accepted/In press) The 17th European Workshop on Reinforcement Learning (EWRL 2024).

    Research output: Chapter in book/report/conference proceeding › Conference abstract › Research › peer review

  11. Published

    AMLTK: A Modular AutoML Toolkit in Python

    Bergman, E., Feurer, M., Bahram, A., Rezaei, A., Purucker, L., Segel, S., Lindauer, M. & Eggensperger, K., 14 Aug 2024, In: The Journal of Open Source Software. 9, 100, 4 p., 6367.

    Research output: Contribution to journal › Article › Research › peer review

  12. Accepted/In press

    Hyperparameter Importance Analysis for Multi-Objective AutoML

    Theodorakopoulos, D., Stahl, F. & Lindauer, M., 4 Jul 2024, (Accepted/In press) Proceedings of the european conference on AI (ECAI).

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  13. Published

    AutoML in Heavily Constrained Applications

    Neutatz, F., Lindauer, M. & Abedjan, Z., Jul 2024, In: VLDB Journal. 33, 4, p. 957–979 23 p.

    Research output: Contribution to journal › Article › Research › peer review

  14. E-pub ahead of print

    Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach

    Deng, D. & Lindauer, M., 10 Jun 2024, (E-pub ahead of print) NeurIPS Workshop on Time Series in the Age of Large Models. (ArXiv).

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  15. Published

    Verfahren zum Trainieren eines Algorithmus des maschinellen Lernens durch ein bestärkendes Lernverfahren

    Eimer, T. (Inventor), Hutter, F. (Inventor), Lindauer, M. (Inventor) & Biedenkapp, A. (Inventor), 4 Apr 2024, IPC No. G06N20/00, Patent No. DE102022210480A1, 4 Oct 2022, Priority date 4 Oct 2022, Priority No. DE202210210480A

    Research output: Patent

  16. E-pub ahead of print

    Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks

    Hennig, L., Tornede, T. & Lindauer, M., 2 Apr 2024, (E-pub ahead of print) 5th Workshop on practical ML for limited/low resource settings.

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  17. Accepted/In press

    auto-sktime: Automated Time Series Forecasting

    Zöller, M., Lindauer, M. & Huber, M., Apr 2024, (Accepted/In press) Proceedings of the 18TH Learning and Intelligent Optimization Conference (LION).

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  18. E-pub ahead of print

    Structure in Deep Reinforcement Learning: A Survey and Open Problems

    Mohan, A., Zhang, A. & Lindauer, M., Apr 2024, (E-pub ahead of print) In: Journal of Artificial Intelligence Research.

    Research output: Contribution to journal › Article › Research › peer review

  19. Published

    Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning

    Giovanelli, J., Tornede, A., Tornede, T. & Lindauer, M., 24 Mar 2024, Proceedings of the 38th conference on AAAI. Wooldridge, M., Dy, J. & Natarajan, S. (eds.). p. 12172-12180 9 p. (Proceedings of the AAAI Conference on Artificial Intelligence; vol. 38, no. 11).

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  20. E-pub ahead of print

    AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks

    Tornede, A., Deng, D., Eimer, T., Giovanelli, J., Mohan, A., Ruhkopf, T., Segel, S., Theodorakopoulos, D., Tornede, T., Wachsmuth, H. & Lindauer, M., 9 Feb 2024, (E-pub ahead of print) In: Transactions on Machine Learning Research.

    Research output: Contribution to journal › Article › Research › peer review

  21. E-pub ahead of print

    ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning

    Becktepe, J., Dierkes, J., Benjamins, C., Mohan, A., Salinas, D., Rajan, R., Hutter, F., Hoos, H., Lindauer, M. & Eimer, T., 2024, (E-pub ahead of print) 17th European Workshop on Reinforcement Learning (EWRL 2024).

    Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

  22. Published

    Bayesian Optimisation for Protein Sequence Design: Gaussian Processes with Zero-Shot Protein Language Model Prior Mean

    Benjamins, C., Surana, S., Bent, O., Lindauer, M. & Duckworth, P., 2024. 12 p.

    Research output: Contribution to conference › Paper › Research › peer review

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