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Machine Learning Section

Organisational unit: Research Group

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Publications

  1. 2025
  2. 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

  3. 2024
  4. E-pub ahead of print

    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., Dec 2024, (E-pub ahead of print) NeurIPS Workshop on Time Series in the Age of Large Models.

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

  5. 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

  6. E-pub ahead of print

    Automl for Multi-Class Anomaly Compensation of Sensor Drift

    Schaller, M. C., Kruse, M., Ortega, A., Lindauer, M. & Rosenhahn, B., Nov 2024, (E-pub ahead of print).

    Research output: Working paper/Preprint › Preprint

  7. 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

  8. 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

  9. 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

  10. 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

  11. E-pub ahead of print

    Position: Why We Must Rethink Empirical Research in Machine Learning

    Herrmann, M., Lange, F. J. D., Eggensperger, K., Casalicchio, G., Wever, M., Feurer, M., Rügamer, D., Hüllermeier, E., Boulesteix, A. & Bischl, B., Jul 2024, (E-pub ahead of print) Proceedings of the international conference on machine learning.

    Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review

  12. E-pub ahead of print

    ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data

    Margraf, V., Wever, M., Gilhuber, S., Tavares, G. M., Seidl, T. & Hüllermeier, E., 15 Jun 2024, (E-pub ahead of print).

    Research output: Working paper/Preprint › Preprint

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. E-pub ahead of print

    Information Leakage Detection through Approximate Bayes-optimal Prediction

    Gupta, P., Wever, M. & Hüllermeier, E., 25 Jan 2024, (E-pub ahead of print).

    Research output: Working paper/Preprint › Preprint

  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. E-pub ahead of print

    Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization

    Benjamins, C., Cenikj, G., Nikolikj, A., Mohan, A., Eftimov, T. & Lindauer, M., 2024, (E-pub ahead of print) Genetic and Evolutionary Computation Conference (GECCO).

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

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