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
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 (Hrsg.), Proceedings of the 12th Argument Mining Workshop (S. 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 (Hrsg.), Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025) (S. 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. (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
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 Vorabveröffentlichung online. 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. Vorabveröffentlichung online. 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) Vorabveröffentlichung online. 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. Vorabveröffentlichung online. https://arxiv.org/abs/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
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 (Hrsg.), Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue (S. 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 Vorabveröffentlichung online. 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 Vorabveröffentlichung online.
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 Vorabveröffentlichung online. 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)
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 (S. 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), Artikel 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). Vorabveröffentlichung online.
Mladenovic, S., Lindauer, M., & Doerr, C. (2025). Automated Data Preparation for Machine Learning. In 4th International Conference on Automated Machine Learning: Non-Archival Track Vorabveröffentlichung online. 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) Vorabveröffentlichung online. 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 (Hrsg.), Proceedings of the 12th Argument Mining Workshop (S. 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 ) (S. 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 (Hrsg.), Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (S. 7458-7485). (Long Papers; Band 1). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2025.naacl-long.382
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, 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
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 (Hrsg.), Findings of the Association for Computational Linguistics: EMNLP 2025 (S. 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 Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Band 1). Association for Computational Linguistics. https://aclanthology.org/2025.naacl-long.122.pdf
Stahl, M., Ziegenbein, T., Park, J., & Wachsmuth, H. (2025). ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentation. In W. Che, J. Nabende, E. Shutova, & M. T. Pilehvar (Hrsg.), Findings of the Association for Computational Linguistics: ACL 2025 (S. 11103–11127). (Proceedings of the Annual Meeting of the Association for Computational Linguistics). https://doi.org/10.18653/v1/2025.findings-acl.579
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
Alshomary, M., Lange, F., Booshehri, M., Sengupta, M., Cimiano, P., & Wachsmuth, H. (2024). Modeling the Quality of Dialogical Explanations. In N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, & N. Xue (Hrsg.), 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings (S. 11523-11536). European Language Resources Association (ELRA). https://doi.org/10.48550/arXiv.2403.00662
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) Vorabveröffentlichung online. 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 Vorabveröffentlichung online.
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
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
Chen, W. F., Alshomary, M., Stahl, M., Al Khatib, K., Stein, B., & Wachsmuth, H. (2024). Reference-guided Style-Consistent Content Transfer. In N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, & N. Xue (Hrsg.), 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings (S. 13754-13768). European Language Resources Association (ELRA).
