Showing results 1 - 42 out of 308
2025
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. Advance online publication. https://doi.org/10.1007/s10503-025-09670-3
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
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., Spliethöver, M., Wever, M. D., Wachsmuth, H., & Lindauer, M. (2025). Towards Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization. In Workshop Track of the AutoML Conference Advance online publication. 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
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.
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 Advance online publication. https://openreview.net/pdf?id=vk7b11DHcW
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 Advance online publication. https://doi.org/10.48550/arXiv.2505.15631
Margraf, V., Naftali-Körner, T., Tornede, A., & Wever, M. D. (Accepted/in press). RunAndSchedule2Survive: Algorithm Scheduling Based on Run2Survive. ACM Transactions on Evolutionary Learning and Optimization.
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
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
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 (Vol. 1). Association for Computational Linguistics. https://aclanthology.org/2025.naacl-long.122.pdf
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
The LIGO Scientific Collaboration, the KAGRA Collaboration, The Virgo Collaboration, Bode, N., Brinkmann, M., Carlassara, M., Chakraborty, P., Danzmann, K., Heurs, M., Johny, N., Knust, N., Lehmann, J., Lück, H., Matiushechkina, M., Nery, M., Schulte, B. W., Vahlbruch, H., Wilken, D., Willke, B., ... Weßels, P. (2025). Search for Continuous Gravitational Waves from Known Pulsars in the First Part of the Fourth LIGO-Virgo-KAGRA Observing Run. Astrophysical Journal, 983(2), Article 99. https://doi.org/10.3847/1538-4357/adb3a0
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 (Eds.), 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings (pp. 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) 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
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 (Eds.), 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings (pp. 13754-13768). European Language Resources Association (ELRA).
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
El Baff, R., Khatib, K. A., Alshomary, M., Konen, K., Stein, B., & Wachsmuth, H. (2024). Improving Argument Effectiveness Across Ideologies using Instruction-tuned Large Language Models. In Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2024 (pp. 4604-4622). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-emnlp.265
Faggioli, G., Dietz, L., Clarke, C. L. A., Demartini, G., Hagen, M., Hauff, C., Kando, N., Kanoulas, E., Potthast, M., Stein, B., & Wachsmuth, H. (2024). Who Determines What Is Relevant? Humans or AI? Why Not Both? A spectrum of human–artificial intelligence collaboration in assessing relevance. Communications of the ACM, 67(4), 31-34. https://doi.org/10.1145/3624730
Feldhus, N., Anagnostopoulou, A., Wang, Q., Alshomary, M., Wachsmuth, H., Sonntag, D., & Möller, S. (2024). Towards Modeling and Evaluating Instructional Explanations in Teacher-Student Dialogues. In Proceedings of the 2024 International Conference on Information Technology for Social Good (pp. 225–230). Association for Computing Machinery. https://doi.org/10.1145/3677525.3678665
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
Kiesel, J., Çöltekin, Ç., Heinrich, M., Fröbe, M., Alshomary, M., De Longueville, B., Erjavec, T., Handke, N., Kopp, M., Ljubešić, N., Meden, K., Mirzhakhmedova, N., Morkevičius, V., Reitis-Münstermann, T., Scharfbillig, M., Stefanovitch, N., Wachsmuth, H., Potthast, M., & Stein, B. (2024). Overview of Touché 2024: Argumentation Systems. In N. Goharian, N. Tonellotto, Y. He, A. Lipani, G. McDonald, C. Macdonald, & I. Ounis (Eds.), Advances in Information Retrieval - 46th European Conference on Information Retrieval, ECIR 2024, Proceedings (pp. 466-473). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14612 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-56069-9_64
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 (pp. 30566 - 30584). Article 1231 https://dl.acm.org/doi/10.5555/3692070.3693301
Mirzakhmedova, N., Kiesel, J., Alshomary, M., Heinrich, M., Handke, N., Cai, X., Barriere, V., Dastgheib, D., Ghahroodi, O., SadraeiJavaheri, M., Asgari, E., Kawaletz, L., Wachsmuth, H., & Stein, B. (2024). The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments. In N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, & N. Xue (Eds.), Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 16121-16134). ELRA and ICCL. https://aclanthology.org/2024.lrec-main.1402/
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)
