Showing results 1 - 42 out of 292
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)
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, 2025(82), 102–129. https://doi.org/10.1515/zfal-2025-2003
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. (Accepted/in press). 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
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). (2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings). 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) Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation (SIGEVO). Advance online publication.
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. In NeurIPS Workshop on Time Series in the Age of Large Models (ArXiv). Advance online publication. https://arxiv.org/abs/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). Position Paper: A Call to Action for a Human-Centered AutoML Paradigm. In Proceedings of the international conference on machine learning Advance online publication.
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. Advance online publication. https://arxiv.org/abs/2306.16021
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
Scharlau, I., Körber, M., Sengupta, M., & Wachsmuth, H. (2024). When to use a metaphor: Metaphors in dialogical explanations with addressees of different expertise. Frontiers in Language Sciences, 3, Article 1474924. https://doi.org/10.3389/flang.2024.1474924
Sengupta, M., El Baff, R., Alshomary, M., & Wachsmuth, H. (2024). Analyzing the Use of Metaphors in News Editorials for Political Framing. In K. Duh, H. Gomez, & S. Bethard (Eds.), Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 3621–3631). (Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024; Vol. 1). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2024.naacl-long.199
Spliethöver, M., Menon, S. N., & Wachsmuth, H. (2024). Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness. In L.-W. Ku, A. Martins, & V. Srikumar (Eds.), Findings of the Association for Computational Linguistics ACL 2024 (pp. 9294-9313). (Proceedings of the Annual Meeting of the Association for Computational Linguistics). https://doi.org/10.18653/v1/2024.findings-acl.553
Stahl, M., Michel, N., Kilsbach, S., Schmidtke, J., Rezat, S., & Wachsmuth, H. (2024). A School Student Essay Corpus for Analyzing Interactions of Argumentative Structure and Quality. In K. Duh, H. Gomez, & S. Bethard (Eds.), Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 2661–2674) https://doi.org/10.18653/v1/2024.naacl-long.145, https://doi.org/10.48550/arXiv.2404.02529
Stahl, M., Biermann, L., Nehring, A., & Wachsmuth, H. (2024). Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation. In Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024) (pp. 283–298)
Theodorakopoulos, D., Stahl, F., & Lindauer, M. (Accepted/in press). Hyperparameter Importance Analysis for Multi-Objective AutoML. In Proceedings of the european conference on AI (ECAI)
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
Wachsmuth, H., Lapesa, G., Cabrio, E., Lauscher, A., Park, J., Vecchi, E. M., Villata, S., & Ziegenbein, T. (2024). Argument Quality Assessment in the Age of Instruction-Following Large Language Models. 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. 1519-1538). ELRA and ICCL. https://doi.org/10.48550/arXiv.2403.16084
Ziegenbein, T., Skitalinska, G., Bayat Makou, A., & Wachsmuth, H. (2024). LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Vol. 1, pp. 4455-4476). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2406.03363, https://doi.org/10.18653/v1/2024.acl-long.244
Ziegenbein, T., Syed, S., Potthast, M., & Wachsmuth, H. (2024). Objective Argument Summarization in Search. In P. Cimiano, A. Frank, M. Kohlhase, & B. Stein (Eds.), Robust Argumentation Machines - First International Conference, RATIO 2024, Proceedings: First International Conference, RATIO 2024, Bielefeld, Germany, June 5–7, 2024, Proceedings (1. ed., pp. 335-351). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14638 LNAI). Springer. https://doi.org/10.1007/978-3-031-63536-6_20
Zöller, M., Lindauer, M., & Huber, M. (Accepted/in press). auto-sktime: Automated Time Series Forecasting. In Proceedings of the 18TH Learning and Intelligent Optimization Conference (LION) https://arxiv.org/abs/2312.08528
2023
Alshomary, M., & Wachsmuth, H. (2023). Conclusion-based Counter-Argument Generation. In EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference (pp. 957-967). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2301.09911
Bäumer, F., Chen, W. F., Geierhos, M., Kersting, J., & Wachsmuth, H. (2023). Dialogue-Based Requirement Compensation and Style-Adjusted Data-To-Text Generation. In On-The-Fly Computing : Individualized IT-Services in dynamic markets (pp. 65-84) https://doi.org/10.5281/zenodo.8068456
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, 2023(6). 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
