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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, Artikel 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 (Hrsg.), Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (S. 3621–3631). (Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024; Band 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 (Hrsg.), Findings of the Association for Computational Linguistics ACL 2024 (S. 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 (Hrsg.), Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (S. 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) (S. 283–298)
Theodorakopoulos, D., Stahl, F., & Lindauer, M. (2024). Hyperparameter Importance Analysis for Multi-Objective AutoML. In U. Endriss, F. S. Melo, K. Bach, A. Bugarin-Diz, J. M. Alonso-Moral, S. Barro, & F. Heintz (Hrsg.), Proceedings of the european conference on AI (ECAI) (S. 1100-1107). (Frontiers in Artificial Intelligence and Applications; Band 392). https://doi.org/10.3233/FAIA240602, https://doi.org/10.48550/arXiv.2405.07640
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. Vorabveröffentlichung online. 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 (Hrsg.), Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (S. 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 L.-W. Ku, A. F. T. Martins, & V. Srikumar (Hrsg.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Band 1, S. 4455-4476). (Proceedings of the Annual Meeting of the Association for Computational Linguistics; Band 1). 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 (Hrsg.), Robust Argumentation Machines - First International Conference, RATIO 2024, Proceedings: First International Conference, RATIO 2024, Bielefeld, Germany, June 5–7, 2024, Proceedings (1. Aufl., S. 335-351). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 14638 LNAI). Springer. https://doi.org/10.1007/978-3-031-63536-6_20
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 (S. 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 (S. 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. Vorabveröffentlichung online. 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) Vorabveröffentlichung online. https://openreview.net/forum?id=DJgHzXv61b
Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Angenommen/im Druck). Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. In AutoML Conference 2023 PMLR.
Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (2023). Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. In GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference Companion (S. 483 - 486). Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation (SIGEVO). https://doi.org/10.1145/3583133
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), Artikel 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) Vorabveröffentlichung online. https://openreview.net/forum?id=N3IDYxLxgtW
Eimer, T., Lindauer, M., & Raileanu, R. (2023). Hyperparameters in Reinforcement Learning and How to Tune Them. In ICML'23: Proceedings of the 40th International Conference on Machine Learning (S. 9104–9149). Artikel 366 https://doi.org/10.48550/arXiv.2306.01324, https://doi.org/10.5555/3618408.3618774
Faggioli, G., Clarke, C. L. A., Demartini, G., Hagen, M., Hauff, C., Kando, N., Kanoulas, E., Potthast, M., Stein, B., Wachsmuth, H., & Dietz, L. (2023). Perspectives on Large Language Models for Relevance Judgment. In ICTIR '23: Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval (S. 39-50). Association for Computing Machinery, Inc. https://doi.org/10.48550/arXiv.2304.09161, https://doi.org/10.1145/3578337.3605136
Haake, C.-J., Auf Der Heide, F. M., Platzner, M., Wachsmuth, H., & Wehrheim, H. (2023). On-The-Fly Computing: Individualized IT-Services in dynamic markets. (Verlagsschriftenreihe des Heinz Nixdorf Instituts; Band 412). Verlagschriftenreihe des Heinz Nixdorf Instituts. https://doi.org/10.17619/UNIPB/1-1797
Hanselle, J., Hüllermeier, E., Mohr, F., Ngomo, A. C. N., Sherif, M. A., Tornede, A., & Wever, M. D. (2023). Configuration and Evaluation. In On-The-Fly Computing -- Individualized IT-services in dynamic markets https://doi.org/10.5281/zenodo.8068466
Kiesel, J., Alshomary, M., Mirzakhmedova, N., Heinrich, M., Handke, N., Wachsmuth, H., & Stein, B. (2023). SemEval-2023 Task 4: ValueEval: Identification of Human Values Behind Arguments. In A. K. Ojha, A. S. Doğruöz, G. Da San Martino, H. T. Madabushi, R. Kumar, & E. Sartori (Hrsg.), Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023) (S. 2287-2303). Association for Computational Linguistics (ACL). https://doi.org/10.18653/V1/2023.SEMEVAL-1.313
Lapesa, G., Vecchi, E. M., Villata, S., & Wachsmuth, H. (2023). Mining, Assessing, and Improving Arguments in NLP and the Social Sciences. In R. Klinger, N. Okazaki, N. Calzolari, & M.-Y. Kan (Hrsg.), Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts (S. 26-32). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.eacl-tutorials.1
Loni, M., Mohan, A., Asadi, M., & Lindauer, M. (Angenommen/im Druck). Learning Activation Functions for Sparse Neural Networks. In Second International Conference on Automated Machine Learning PMLR. https://arxiv.org/abs/2305.10964
Mallik, N., Bergman, E., Hvarfner, C., Stoll, D., Janowski, M., Lindauer, M., Nardi, L., & Hutter, F. (2023). PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning. In NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing Systems (S. 7377 - 7391). Artikel 323 https://doi.org/10.48550/arXiv.2306.12370
Mohan, A., Zhang, A., & Lindauer, M. (Angenommen/im Druck). A Patterns Framework for Incorporating Structure in Deep Reinforcement Learning. In The 16th European Workshop on Reinforcement Learning (EWRL 2023) https://openreview.net/forum?id=KkKWsPLlAx
Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A., & Lindauer, M. (2023). AutoRL Hyperparameter Landscapes. In Conference proceeding: Second Internatinal Conference on Automated Machine Learning (Proceedings of Machine Learning Research; Band 228). PMLR. https://doi.org/10.48550/arXiv.2304.02396
Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A., & Lindauer, M. (2023). Extended Abstract: AutoRL Hyperparameter Landscapes. Abstract von European Workshop on Reinforcement Learning 2023, Brüssel. Vorabveröffentlichung online. https://openreview.net/forum?id=4Zu0l5lBgc
Nouri, Z., Prakash, N., Gadiraju, U., & Wachsmuth, H. (2023). Supporting Requesters in Writing Clear Crowdsourcing Task Descriptions Through Computational Flaw Assessment. In IUI 2023 - Proceedings of the 28th International Conference on Intelligent User Interfaces (S. 737–749). Association for Computing Machinery (ACM). https://doi.org/10.1145/3581641.3584039
Ruhkopf, T., Mohan, A., Deng, D., Tornede, A., Hutter, F., & Lindauer, M. (2023). MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information. Transactions on Machine Learning Research. Vorabveröffentlichung online. https://openreview.net/forum?id=5aYGXxByI6
Schede, E., Brandt, J., Tornede, A., Wever, M., Bengs, V., Hüllermeier, E., & Tierney, K. (2023). A Survey of Methods for Automated Algorithm Configuration (Extended Abstract). In E. Elkind (Hrsg.), Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023: Jorunal Track (S. 6964-6968). (IJCAI International Joint Conference on Artificial Intelligence; Band 2023-August). AAAI Press/International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2023/791
Schubert, F., Benjamins, C., Döhler, S., Rosenhahn, B., & Lindauer, M. (2023). POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. Transactions on Machine Learning Research, 2023(4). https://doi.org/10.48550/arXiv.2205.11357
Segel, S., Graf, H., Tornede, A., Bischl, B., & Lindauer, M. (2023). Symbolic Explanations for Hyperparameter Optimization. In AutoML Conference 2023 PMLR. Vorabveröffentlichung online. https://openreview.net/forum?id=JQwAc91sg_x
Sengupta, M. (2023). Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms. In Findings of the Association for Computational Linguistics: EMNLP 2023 (S. 4636–4659). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.findings-emnlp.308
Shoaib, M., Kotthoff, L., Lindauer, M., & Kant, S. (2023). AutoML: advanced tool for mining multivariate plant traits. Trends in Plant Science, 28(12), 1451-1452. https://doi.org/10.1016/j.tplants.2023.09.008
Skitalinskaya, G., Spliethöver, M., & Wachsmuth, H. (2023). Claim Optimization in Computational Argumentation. In C. M. Keet, H.-Y. Lee, & S. Zarrieß (Hrsg.), Proceedings of the 16th International Natural Language Generation Conference (S. 134-152). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2212.08913, https://doi.org/10.18653/v1/2023.inlg-main.10
Skitalinskaya, G., & Wachsmuth, H. (2023). To Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (S. 15799–15816). (Proceedings of the Annual Meeting of the Association for Computational Linguistics; Band 1). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.acl-long.880
Stahl, M., & Wachsmuth, H. (2023). Identifying Feedback Types to Augment Feedback Comment Generation. In Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges (S. 31-36) https://aclanthology.org/2023.inlg-genchal.5
Stahl, M., Düsterhus, N., Chen, M.-H., & Wachsmuth, H. (2023). Mind the Gap: Automated Corpus Creation for Enthymeme Detection and Reconstruction in Learner Arguments. In Findings of the Association for Computational Linguistics: EMNLP 2023 (S. 4703-4717) https://doi.org/10.48550/arXiv.2310.18098, https://doi.org/10.18653/v1/2023.findings-emnlp.312
