Showing results 1 - 42 out of 287
2025
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
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. In NeurIPS Workshop on Time Series in the Age of Large Models Advance online publication.
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
Schaller, M. C., Kruse, M., Ortega, A., Lindauer, M., & Rosenhahn, B. (2024). Automl for Multi-Class Anomaly Compensation of Sensor Drift. Advance online publication. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5043128
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, 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).
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). (Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024; Vol. 1).
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 Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
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)
Ziegenbein, T., Syed, S., Potthast, M., & Wachsmuth, H. (2024). Objective Argument Summarization in Search. In Conference on Advances in Robust Argumentation Machines
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
Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Accepted/in press). Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. In AutoML Conference 2023 PMLR.
Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Accepted/in press). Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. In GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference Companion Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation (SIGEVO).
Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Thomas, J., Ullmann, T., Becker, M., Boulesteix, A.-L., Deng, D., & Lindauer, M. (2023). Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(2), Article e1484. https://doi.org/10.1002/widm.1484
Denkena, B., Dittrich, M.-A., Noske, H., Lange, D., Benjamins, C., & Lindauer, M. (2023). Application of machine learning for fleet-based condition monitoring of ball screw drives in machine tools. The international journal of advanced
manufacturing technology, 127(3-4), 1143-1164. https://doi.org/10.1007/s00170-023-11524-9
Eimer, T., Lindauer, M., & Raileanu, R. (2023). Extended Abstract: Hyperparameters in Reinforcement Learning and How To Tune Them. In The 16th European Workshop on Reinforcement Learning (EWRL 2023) Advance online publication. https://openreview.net/forum?id=N3IDYxLxgtW
