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. Advance online publication. https://doi.org/10.48550/arXiv.2506.06143
Che, M., Tseng, T.-Y., Eimer-Rüegg, T., Lindauer, M., & von Rohr, A. (Accepted/In press). Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL. Paper presented at 3rd Reinforcement Learning Conference 2026, RLC'26, Montréal, Canada.
Deng, D. (2026). Neural Architecture Search Space Design: From Coarse to Fine-Grained. [Doctoral thesis, Leibniz University 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 Advance online publication. 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 Advance online publication. 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. Advance online publication. https://openreview.net/forum?id=Euti6LHIOs
Pierro, A., Yik, J., Timcheck, J., Lindauer, M., Hüllermeier, E., & Wever, M. D. (2026). Evolutionary Mapping of Neural Networks to Spatial Accelerators. In GECCO '26: Proceedings of the Genetic and Evolutionary Computation Conference (pp. 329-337) https://doi.org/10.1145/3795095.3805135
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. Advance online publication. https://doi.org/10.48550/arXiv.2601.03166
Wever, M. D., Muschalik, M., Fumagalli, F., & Lindauer, M. (Accepted/In press). HyperSHAP: Shapley Values and Interactions for Explaining Hyperparameter Optimization. In Proceedings of the Fortieth AAAI Conference on Artificial Intelligence (AAAI 2026)
Ziegenbein, T., Stahl, M., & Wachsmuth, H. (2026). Teaching LLMs Human-Like Editing of Inappropriate Argumentation via Reinforcement Learning. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 38616–38637). Association for Computational Linguistics. https://doi.org/10.48550/arXiv.2604.12770, https://doi.org/10.18653/v1/2026.acl-long.1789
2025
Ajjour, Y., & Wachsmuth, H. (2025). Exploring LLM Priming Strategies for Few-Shot Stance Classification. In E. Chistova, P. Cimiano, S. Haddadan, G. Lapesa, & R. Ruiz-Dolz (Eds.), Proceedings of the 12th Argument Mining Workshop (pp. 11-23). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.argmining-1.2
Anagnostopoulou, A., Feldhus, N., Hsu, Y.-S., Alshomary, M., Wachsmuth, H., & Sonntag, D. (2025). Human and LLM-based Assessment of Teaching Acts in Expert-led Explanatory Dialogues. In M. Strube, C. Braud, C. Hardmeier, J. J. Li, S. Loaiciga, A. Zeldes, & C. Li (Eds.), Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025) (pp. 166-181). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.codi-1.15
Becktepe, J., Hennig, L., Oeltze-Jafra, S., & Lindauer, M. (Accepted/In press). Auto-nnU-Net: Towards Automated Medical Image Segmentation. In International Conference on Automated Machine Learning 2025 https://openreview.net/pdf?id=XSTIEVoEa2
Chen, M. H., Chen, W. F., Mudgal, G., & Wachsmuth, H. (2025). Cross-Cultural Comparison of Argument Structures Among English Learners: Argument Proficiency, Patterns, and Communication Styles. ARGUMENTATION, 39(4), 571-599. https://doi.org/10.1007/s10503-025-09670-3
Deng, D., & Lindauer, M. (2025). Neural Attention Search. In The Thirty-Ninth Annual Conference on Neural Information Processing Systems Advance online publication. 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. Advance online publication. 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) Advance online publication. 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. Advance online publication. https://doi.org/10.48550/arXiv.2512.16491
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)
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
Fichtel, L., Spliethöver, M., Hüllermeier, E., Jimenez, P., Klowait, N., Kopp, S., Ngonga Ngomo, A.-C., Robrecht, A., Scharlau, I., Terfloth, L., Vollmer, A.-L., & Wachsmuth, H. (2025). Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues. In F. Béchet, F. Lefèvre, N. Asher, S. Kim, & T. Merlin (Eds.), Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue (pp. 1-20). Association for Computational Linguistics. https://aclanthology.org/2025.sigdial-1.1/
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 Advance online publication. 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), Article 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 Advance online publication.
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. https://doi.org/10.48550/arXiv.2606.23208
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)
Kilsbach, S., Rezat, S., Michel, N., Karabey, R., Stahl, M., & Wachsmuth, H. (2025). Mehrebenenannotation argumentativer Lerner∗innentexte für die automatische Textauswertung. Zeitschrift fur Angewandte Linguistik, 82(1), 102–129. https://doi.org/10.1515/zfal-2025-2003
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 (pp. 50-59) https://doi.org/10.1109/CCGridW65158.2025.00017, https://doi.org/10.48550/arXiv.2505.15631
Margraf, V., Naftali-Körner, T., Tornede, A., & Wever, M. D. (2025). RunAndSchedule2Survive: Algorithm Scheduling Based on Run2Survive. ACM Transactions on Evolutionary Learning and Optimization, 5(3), Article 21. https://doi.org/10.1145/3737705
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.
Mladenovic, S., Lindauer, M., & Doerr, C. (2025). Automated Data Preparation for Machine Learning. In 4th International Conference on Automated Machine Learning: Non-Archival Track Advance online publication. 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) Advance online publication. https://openreview.net/pdf?id=QlDXH5NkUx
Musi, E., Kökciyan, N., Khatib, K. A., Ceolin, D., Dietz, E., Gutekunst, K. M., Hautli-Janisz, A., Santibáñez, C., Schneider, J., Scholz, J., Steging, C., Visser, J., & Wachsmuth, H. (2025). Toward Reasonable Parrots: Why Large Language Models Should Argue with Us by Design. In E. Chistova, P. Cimiano, S. Haddadan, G. Lapesa, & R. Ruiz-Dolz (Eds.), Proceedings of the 12th Argument Mining Workshop (pp. 24-31). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.argmining-1.3
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
Rezat, S., Kilsbach, S., Karabey, R., Michel, N., Stahl, M., & Wachsmuth, H. (2025). Didaktische Modellierung automatisierten adaptiven Feedbacks zu argumentativen Lerner* innentexten. Leseräume: Zeitschrift für Literalität in Schule und Forschung, 12(11). https://xn--leserume-4za.de/wp-content/uploads/2025/06/Rezat-et-al-2025-LR-JG12-H11.pdf
Romberg, J., Maurer, M., Wachsmuth, H., & Lapesa, G. (2025). Towards a Perspectivist Turn in Argument Quality Assessment. In L. Chiruzzo, A. Ritter, & L. Wang (Eds.), Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 7458-7485). (Long Papers; Vol. 1). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2025.naacl-long.382
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
Segel, S., Graf, H., Bergman, E., Thieme, K., Wever, M. D., Tornede, A., Hutter, F., & Lindauer, M. (Accepted/In press). 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
Sengupta, M., Muschalik, M., Fumagalli, F., Hammer, B., Hüllermeier, E., Ghosh, D., & Wachsmuth, H. (2025). Investigating the Impact of Conceptual Metaphors on LLM-based NLI through Shapley Interactions. In C. Christodoulopoulos, T. Chakraborty, C. Rose, & V. Peng (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2025 (pp. 17393-17403). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.findings-emnlp.942
Spliethöver, M., Knebler, T., Fumagalli, F., Muschalik, M., Hammer, B., Hüllermeier, E., & Wachsmuth, H. (2025). Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection. In L. Chiruzzo, A. Ritter, & L. Wang (Eds.), Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Vol. 1, pp. 2421-2449). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.naacl-long.122
