2026
Benjamins, C., Graf, H., Segel, S., Deng, D., Ruhkopf, T., Hennig, L., Basu, S., Mallik, N., Bergman, E., Chen, D., Clement, F., Tornede, A., Feurer, M., Eggensperger, K., Hutter, F., Doerr, C., & Lindauer, M. (2026). carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks. Transactions on Machine Learning Research. Vorzeitige Online-Publikation. https://doi.org/10.48550/arXiv.2506.06143
Che, M., Tseng, T.-Y., Eimer-Rüegg, T., Lindauer, M., & von Rohr, A. (2026). Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL. Beitrag in 3rd Reinforcement Learning Conference 2026, RLC'26, Montréal, Kanada.
Deng, D. (2026). Neural Architecture Search Space Design: From Coarse to Fine-Grained. [Dissertation, Gottfried Wilhelm Leibniz Universität Hannover]. Institutionelles Repositorium Leibniz Universität Hannover. https://doi.org/10.15488/21360
Deng, D., Winje, A. B., Fehring, L., & Lindauer, M. (2026). Neural Attention Search Linear: Towards Adaptive Token-Level Hybrid Attention Models. in Proceedings of NeurIPS Vorzeitige Online-Publikation. https://arxiv.org/pdf/2602.03681
Fehring, L., Wever, M., Spliethöver, M., Hennig, L., Wachsmuth, H., & Lindauer, M. (2026). Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization. in Proceedings of the AutoML Conference (Band abs/2511.02570) https://doi.org/10.48550/arXiv.2511.02570
Mahlau, Y., Augenstein, Y., Hughes, T., Lindauer, M., & Rosenhahn, B. (2026). Gradient-Informed Bayesian and Interior Point Optimization for Efficient Inverse Design in Nanophotonics. Optics Express, 34(13), 23160-23174. https://doi.org/10.1364/OE.600198
Mladenovic, S., Lindauer, M., & Doerr, C. (2026). Automated Data Preparation for Machine Learning: A Survey. Data-centric Machine Learning Research. Vorzeitige Online-Publikation. https://openreview.net/forum?id=Euti6LHIOs
Rook, J., Benjamins, C., Bossek, J., Trautmann, H., Hoos, H., & Lindauer, M. (2026). MO-SMAC: Multi-objective Sequential Model-based Algorithm Configuration. Evolutionary computation, 34(1), 29-52. https://doi.org/10.1162/evco_a_00371
Theodorakopoulos, D., Wever, M., & Lindauer, M. (2026). Dynamic Hyperparameter Importance for Efficient Multi-Objective Optimization. https://doi.org/10.48550/arXiv.2601.03166
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
Becktepe, J., Hennig, L., Oeltze-Jafra, S., & Lindauer, M. (Angenommen/Im Druck). Auto-nnU-Net: Towards Automated Medical Image Segmentation. in International Conference on Automated Machine Learning 2025 https://openreview.net/pdf?id=XSTIEVoEa2
Deng, D., & Lindauer, M. (2025). Neural Attention Search. in The Thirty-Ninth Annual Conference on Neural Information Processing Systems Vorzeitige Online-Publikation. https://doi.org/10.48550/arXiv.2502.13251
Deng, D., & Lindauer, M. (2025). Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach. Transactions on Machine Learning Research, 2025-October. Vorzeitige Online-Publikation. https://doi.org/10.48550/arXiv.2406.05088
Dierkes, J., Eimer, T., Lindauer, M., & Hoos, H. (2025). Performance Prediction In Reinforcement Learning: The Bad And The Ugly. in 18th European Workshop on Reinforcement Learning (EWRL) Vorzeitige Online-Publikation. https://openreview.net/pdf?id=L9J6Xmta4J
Eimer, T., Schäpermeier, L., Biedenkapp, A., Tornede, A., Kotthoff, L., Leyman, P., Feurer, M., Eggensperger, K., Maile, K., Tornede, T., Kozak, A., Xue, K., Wever, M. D., Baratchi, M., Pulatov, D., Trautmann, H., Kashgarani, H., & Lindauer, M. (2025). Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Network. Vorzeitige Online-Publikation. https://doi.org/10.48550/arXiv.2512.16491
Fehring, L., Eimer, T., & Lindauer, M. (Angenommen/Im Druck). Growing with Experience: Growing Neural Networks in Deep Reinforcement Learning. in 2025 Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2025)
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
Henheik, M., Eimer, T., & Lindauer, M. (2025). Revisiting Learning Rate Control. in International Conference on Automated Machine Learning 2025 Vorzeitige Online-Publikation.
Hennig, L., & Lindauer, M. (2025). Leveraging AutoML for Sustainable Deep Learning: A MultiObjective HPO Approach on Deep Shift Neural Networks. Transactions on Machine Learning Research, 2025-July.
Hennig, L., & Lindauer, M. (2025). Leveraging AutoML for Sustainable Deep Learning: A Multi- Objective HPO Approach on Deep Shift Neural Networks. in Transactions on Machine Learning Research Vorzeitige Online-Publikation. https://openreview.net/pdf?id=vk7b11DHcW
Jabs, D., Mohan, A., & Lindauer, M. (Angenommen/Im Druck). Moments Matter: Stabilizing Policy Optimization using Return Distributions. in 2025 Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2025)
Kocher, N., Wassermann, C., Hennig, L., Seng, J., Lindauer, M., Hoos, H., Kersting, K., & Müller, M. (2025). 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 (S. 50-59) https://doi.org/10.1109/CCGridW65158.2025.00017, https://doi.org/10.48550/arXiv.2505.15631
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.
Mladenovic, S., Lindauer, M., & Doerr, C. (2025). Automated Data Preparation for Machine Learning. in 4th International Conference on Automated Machine Learning: Non-Archival Track Vorzeitige Online-Publikation. https://openreview.net/forum?id=qjLJgQNipN
Mohan, A., Eimer, T., Benjamins, C., Lindauer, M., & Biedenkapp, A. (2025). Mighty: A Comprehensive Tool for studying Generalization, Meta-RL and AutoRL. in 18th European Workshop on Reinforcement Learning (EWRL) Vorzeitige Online-Publikation. https://openreview.net/pdf?id=QlDXH5NkUx
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 ) (S. 350–363) https://openproceedings.org/2025/conf/edbt/paper-97.pdf
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, Artikel 117097. https://doi.org/10.1016/j.measurement.2025.117097
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
Zöller, M., Lindauer, M., & Huber, M. (2025). auto-sktime: Automated Time Series Forecasting. in P. Festa, D. Ferone, T. Pastore, & O. Pisacane (Hrsg.), Proceedings of the 18TH Learning and Intelligent Optimization Conference (LION) (S. 456–471). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 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) Vorzeitige Online-Publikation. 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. Beitrag in The 38th Annual Conference on Neural Information Processing Systems, Vancouver, Kanada.
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 Vorzeitige Online-Publikation.
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) (S. 563 - 566). Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation (SIGEVO). https://doi.org/10.1145/3638530.3654291
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
Eimer, T., Hutter, F., Lindauer, M., & Biedenkapp, A. (2024). Verfahren zum Trainieren eines Algorithmus des maschinellen Lernens durch ein bestärkendes Lernverfahren. (Patent Nr. 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 (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
Hennig, L., Tornede, T., & Lindauer, M. (2024). Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks. Vorzeitige Online-Publikation. 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). 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
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. (Angenommen/Im Druck). Towards Enhancing Predictive Representations using Relational Structure in Reinforcement Learning. in The 17th European Workshop on Reinforcement Learning (EWRL 2024)
