Funding Agency
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, 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. The foundation of trustful use of AutoML will be based on explanations of its results and processes. To this end, 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 allowing to combine human intuition and generalization capabilities for complex systems, and efficiency of systematic optimization approaches for AutoML.
Lead at LUHAI: Prof. Lindauer
Funding Program: ERC Starting Grant
Project Period: Dec 2022 - Nov 2027
Publications
First
1
2026
Wever, M. D., Muschalik, M., Fumagalli, F., & Lindauer, M. (Angenommen/Im Druck). HyperSHAP: Shapley Values and Interactions for Explaining Hyperparameter Optimization. in Proceedings of the Fortieth AAAI Conference on Artificial Intelligence (AAAI 2026)
2025
Bischl, B., Casalicchio, G., Das, T., Feurer, M., Fischer, S., Gijsbers, P., Mukherjee, S., Müller, A. C., Németh, L., Oala, L., Purucker, L., Ravi, S., van Rijn, J. N., Singh, P., Vanschoren, J., van der Velde, J., & Wever, M. (2025). OpenML: Insights from 10 years and more than a thousand papers. Patterns, 6(7), Artikel 101317. https://doi.org/10.1016/j.patter.2025.101317
Fehring, L., Wever, M., Spliethöver, M., Hennig, L., Wachsmuth, H., & Lindauer, M. (2025). Towards Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization. in Workshop Track of the AutoML Conference https://openreview.net/pdf?id=mQ0IENZRx2
Graf, H., Fehring, L., Tornede, T., Tornede, A., Wever, M. D., & Lindauer, M. (2025). Towards Exploiting Early Termination for Multi-Fidelity Hyperparameter Optimization. in Workshop Track of the AutoML Conference Vorzeitige Online-Publikation. https://openreview.net/pdf?id=apxqygZeFV
Hasebrook, N., Morsbach, F., Kannengießer, N., Zöller, M., Franke, J., Lindauer, M., Hutter, F., & Sunyaev, A. (2025). Practitioner Motives to Use Different Hyperparameter Optimization Methods. ACM Transactions on Computer-Human Interaction, 32(6), Artikel 59. https://doi.org/10.1145/3745771, https://doi.org/10.48550/arXiv.2203.01717
Margraf, V., Lappe, A., Wever, M. D., Benjamins, C., Hüllermeier, E., & Lindauer, M. (2025). SynthACticBench: A Capability-Based Synthetic Benchmark for Algorithm Configuration. in GECCO 2025 - Proceedings of the 2025 Genetic and Evolutionary Computation Conference (ACM Conferences). Association for Computing Machinery (ACM). Vorzeitige Online-Publikation.
Segel, S., Graf, H., Bergman, E., Thieme, K., Wever, M. D., Tornede, A., Hutter, F., & Lindauer, M. (Angenommen/Im Druck). DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning. Journal of Machine Learning Research, 2025(26). http://jmlr.org/papers/v26/24-1353.html
2024
Bergman, E., Feurer, M., Bahram, A., Rezaei, A., Purucker, L., Segel, S., Lindauer, M., & Eggensperger, K. (2024). AMLTK: A Modular AutoML Toolkit in Python. The Journal of Open Source Software, 9(100), Artikel 6367. https://doi.org/10.21105/joss.06367
Giovanelli, J., Tornede, A., Tornede, T., & Lindauer, M. (2024). Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning. in M. Wooldridge, J. Dy, & S. Natarajan (Hrsg.), Proceedings of the 38th conference on AAAI (S. 12172-12180). (Proceedings of the AAAI Conference on Artificial Intelligence; Band 38, Nr. 11). https://doi.org/10.48550/arXiv.2309.03581, https://doi.org/10.1609/aaai.v38i11.29106
Lindauer, M., Karl, F., Klier, A., Moosbauer, J., Tornede, A., Müller, A., Hutter, F., Feurer, M., & Bischl, B. (2024). A Call to Action for a Human-Centered AutoML Paradigm. in Proceedings of the international conference on machine learning (S. 30566 - 30584). Artikel 1231 https://dl.acm.org/doi/10.5555/3692070.3693301
Tornede, A., Deng, D., Eimer, T., Giovanelli, J., Mohan, A., Ruhkopf, T., Segel, S., Theodorakopoulos, D., Tornede, T., Wachsmuth, H., & Lindauer, M. (2024). AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. Transactions on Machine Learning Research. Vorzeitige Online-Publikation. https://doi.org/10.48550/arXiv.2306.08107
2023
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) Vorzeitige Online-Publikation. 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). Artikel 366 https://doi.org/10.48550/arXiv.2306.01324, https://doi.org/10.5555/3618408.3618774
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 NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing Systems (S. 7377 - 7391). Artikel 323 https://doi.org/10.48550/arXiv.2306.12370
Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A., & Lindauer, M. (2023). AutoRL Hyperparameter Landscapes. in Conference proceeding: Second Internatinal Conference on Automated Machine Learning (Proceedings of Machine Learning Research; Band 228). PMLR. https://doi.org/10.48550/arXiv.2304.02396
Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A., & Lindauer, M. (2023). Extended Abstract: AutoRL Hyperparameter Landscapes. Abstract von European Workshop on Reinforcement Learning 2023, Brüssel. https://openreview.net/forum?id=4Zu0l5lBgc
Segel, S., Graf, H., Tornede, A., Bischl, B., & Lindauer, M. (2023). Symbolic Explanations for Hyperparameter Optimization. in AutoML Conference 2023 PMLR. Vorzeitige Online-Publikation. https://openreview.net/forum?id=JQwAc91sg_x
Zoeller, 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): Improving the Quality of Life, SMC 2023 - Proceedings (S. 2907-2912). (IEEE International Conference on Systems, Man, and Cybernetics). IEEE Xplore Digital Library. https://doi.org/10.1109/SMC53992.2023.10394031, https://doi.org/10.48550/arXiv.2306.12215
2022
Hvarfner, C., Stoll, D., Souza, A. L. F., Lindauer, M., Hutter, F., & Nardi, L. (2022). π BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. in Proceedings of the International conference on Learning Representation (ICLR) https://doi.org/10.48550/arXiv.2204.11051
Mallik, N., Hvarfner, C., Stoll, D., Janowski, M., Bergman, E., Lindauer, M. T., Nardi, L., & Hutter, F. (2022). PriorBand: HyperBand + Human Expert Knowledge. in 2022 NeurIPS Workshop on Meta Learning (MetaLearn) https://openreview.net/forum?id=ds21dwfBBH
Moosbauer, J., Casalicchio, G., Lindauer, M., & Bischl, B. (2022). Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. Vorzeitige Online-Publikation. https://doi.org/10.48550/arXiv.2206.05447
