Showing results 1 - 42 out of 138
2025
Fehring, L., Eimer, T., & Lindauer, M. (Accepted/in press). Growing with Experience: Growing Neural Networks in Deep Reinforcement Learning. In 2025 Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2025)
Henheik, M., Eimer, T., & Lindauer, M. (2025). Revisiting Learning Rate Control. In International Conference on Automated Machine Learning 2025 Advance online publication.
Hennig, L., Lindauer, M., Kocher, N., Wassermann, C., Seng, J., Hoos, H., Kersting, K., & Müller, M. (Accepted/in press). Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks. In Castanet 2025 Workshop on Challenges Advances and Sustainability in AI HPC Interaction: In conjunction with the 25th IEEE ACM International Symposium on Cluster Cloud and Internet Computing
Jabs, D., Mohan, A., & Lindauer, M. (Accepted/in press). Moments Matter: Stabilizing Policy Optimization using Return Distributions. In 2025 Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2025)
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). Advance online publication.
Neutatz, F., Lindauer, M., & Abedjan, Z. (2025). How Green is AutoML for Tabular Data? In Proceedings 28th International Conference on Extending Database Technology ( EDBT 2025 ) (pp. 350–363) https://openproceedings.org/2025/conf/edbt/paper-97.pdf
Rook, J., Benjamins, C., Bossek, J., Trautmann, H., Hoos, H., & Lindauer, M. (2025). MO-SMAC: Multi-objective Sequential Model-based Algorithm Configuration. Evolutionary computation, 25(1), 1-25. https://doi.org/10.1162/evco_a_00371
Schaller, M. C., Kruse, M., Ortega, A., Lindauer, M., & Rosenhahn, B. (2025). Automl for Multi-Class Anomaly Compensation of Sensor Drift. Measurement: Journal of the International Measurement Confederation, 250, Article 117097. https://doi.org/10.1016/j.measurement.2025.117097
Zöller, M., Lindauer, M., & Huber, M. (2025). auto-sktime: Automated Time Series Forecasting. In P. Festa, D. Ferone, T. Pastore, & O. Pisacane (Eds.), Proceedings of the 18TH Learning and Intelligent Optimization Conference (LION) (pp. 456–471). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14990 LNCS). https://doi.org/10.1007/978-3-031-75623-8_35, https://doi.org/10.48550/arXiv.2312.08528
2024
Becktepe, J., Dierkes, J., Benjamins, C., Mohan, A., Salinas, D., Rajan, R., Hutter, F., Hoos, H., Lindauer, M., & Eimer, T. (2024). ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. In 17th European Workshop on Reinforcement Learning (EWRL 2024) Advance online publication. https://doi.org/10.48550/arXiv.2409.18827
Benjamins, C., Surana, S., Bent, O., Lindauer, M., & Duckworth, P. (2024). Bayesian Optimisation for Protein Sequence Design: Gaussian Processes with Zero-Shot Protein Language Model Prior Mean. Paper presented at The 38th Annual Conference on Neural Information Processing Systems, Vancouver, Canada.
Benjamins, C., Surana, S., Bent, O., Lindauer, M., & Duckworth, P. (2024). Bayesian Optimization for Protein Sequence Design: Back to Simplicity with Gaussian Processes. In AI for Accelerated Materials Design - NeurIPS Workshop 2024 Advance online publication.
Benjamins, C., Cenikj, G., Nikolikj, A., Mohan, A., Eftimov, T., & Lindauer, M. (2024). Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization. In Genetic and Evolutionary Computation Conference (GECCO) (pp. 563 - 566). Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation (SIGEVO). https://doi.org/10.1145/3638530
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), Article 6367. https://doi.org/10.21105/joss.06367
Deng, D., & Lindauer, M. (2024). Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach. Advance online publication. https://doi.org/10.48550/arXiv.2406.05088
Eimer, T., Hutter, F., Lindauer, M., & Biedenkapp, A. (2024). Verfahren zum Trainieren eines Algorithmus des maschinellen Lernens durch ein bestärkendes Lernverfahren. (Patent No. DE102022210480A1). Deutsches Patent- und Markenamt (DPMA). https://worldwide.espacenet.com/patent/search/family/090246319/publication/DE102022210480A1?q=pn%3DDE102022210480A1
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 (Eds.), Proceedings of the 38th conference on AAAI (pp. 12172-12180). (Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 38, No. 11). https://doi.org/10.48550/arXiv.2309.03581, https://doi.org/10.1609/aaai.v38i11.29106
Hennig, L., Tornede, T., & Lindauer, M. (2024). Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks. In 5th Workshop on practical ML for limited/low resource settings Advance online publication. https://doi.org/10.48550/arXiv.2404.01965
Lindauer, M., Karl, F., Klier, A., Moosbauer, J., Tornede, A., Müller, A., Hutter, F., Feurer, M., & Bischl, B. (2024). Position Paper: A Call to Action for a Human-Centered AutoML Paradigm. In Proceedings of the international conference on machine learning Advance online publication.
Mohan, A., Zhang, A., & Lindauer, M. (2024). Structure in Deep Reinforcement Learning: A Survey and Open Problems. Journal of Artificial Intelligence Research, 79, 1167-1236. https://doi.org/10.1613/jair.1.15703
Mohan, A., & Lindauer, M. (Accepted/in press). Towards Enhancing Predictive Representations using Relational Structure in Reinforcement Learning. In The 17th European Workshop on Reinforcement Learning (EWRL 2024)
Neutatz, F., Lindauer, M., & Abedjan, Z. (2024). AutoML in Heavily Constrained Applications. VLDB Journal, 33(4), 957–979. https://doi.org/10.48550/arXiv.2306.16913, https://doi.org/10.1007/s00778-023-00820-1
Theodorakopoulos, D., Stahl, F., & Lindauer, M. (2024). Hyperparameter Importance Analysis for Multi-Objective AutoML. In U. Endriss, F. S. Melo, K. Bach, A. Bugarin-Diz, J. M. Alonso-Moral, S. Barro, & F. Heintz (Eds.), Proceedings of the european conference on AI (ECAI) (pp. 1100-1107). (Frontiers in Artificial Intelligence and Applications; Vol. 392). https://doi.org/10.3233/FAIA240602, https://doi.org/10.48550/arXiv.2405.07640
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. Advance online publication. https://doi.org/10.48550/arXiv.2306.08107
2023
Benjamins, C., Eimer, T., Schubert, F. G., Mohan, A., Döhler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (2023). Contextualize Me – The Case for Context in Reinforcement Learning. Transactions on Machine Learning Research. Advance online publication. https://doi.org/10.48550/arXiv.2202.04500
Benjamins, C., Eimer, T., Schubert, F. G., Mohan, A., Döhler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (2023). Extended Abstract: Contextualize Me -- The Case for Context in Reinforcement Learning. In The 16th European Workshop on Reinforcement Learning (EWRL 2023) Advance online publication. https://openreview.net/forum?id=DJgHzXv61b
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. (2023). Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. In GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 483 - 486). Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation (SIGEVO). https://doi.org/10.1145/3583133
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), Article e1484. https://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, 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) Advance online publication. 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 (pp. 9104–9149). Article 366 https://doi.org/10.48550/arXiv.2306.01324, https://doi.org/10.5555/3618408.3618774
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. https://arxiv.org/abs/2305.10964
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 Proceedings of the international Conference on Neural Information Processing Systems (NeurIPS) Advance online publication. https://doi.org/10.48550/arXiv.2306.12370
Mohan, A., Zhang, A., & Lindauer, M. (Accepted/in press). A Patterns Framework for Incorporating Structure in Deep Reinforcement Learning. In The 16th European Workshop on Reinforcement Learning (EWRL 2023) https://openreview.net/forum?id=KkKWsPLlAx
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; Vol. 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 from European Workshop on Reinforcement Learning 2023, Brüssel. Advance online publication. https://openreview.net/forum?id=4Zu0l5lBgc
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. Advance online publication. https://openreview.net/forum?id=5aYGXxByI6
Schubert, F., Benjamins, C., Döhler, S., Rosenhahn, B., & Lindauer, M. (2023). POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. Transactions on Machine Learning Research, 2023(4). https://doi.org/10.48550/arXiv.2205.11357
Segel, S., Graf, H., Tornede, A., Bischl, B., & Lindauer, M. (2023). Symbolic Explanations for Hyperparameter Optimization. In AutoML Conference 2023 PMLR. Advance online publication. https://openreview.net/forum?id=JQwAc91sg_x
Shoaib, M., Kotthoff, L., Lindauer, M., & Kant, S. (2023). AutoML: advanced tool for mining multivariate plant traits. Trends in Plant Science, 28(12), 1451-1452. https://doi.org/10.1016/j.tplants.2023.09.008
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 https://www.climatechange.ai/papers/iclr2023/53
