Zeige Ergebnisse 43 - 84 von 300
2024
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 Proceedings of the international Conference on Neural Information Processing Systems (NeurIPS) Vorabveröffentlichung online. 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
Syed, S., Ziegenbein, T., Heinisch, P., Wachsmuth, H., & Potthast, M. (2023). Frame-oriented Summarization of Argumentative Discussions. In Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue (S. 114-129). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.sigdial-1.10
Theodorakopoulos, D., Manß, C., Stahl, F., & Lindauer, M. (2023). Green-AutoML for Plastic Litter Detection. In Proceedings of the ICLR Workshop on Tackling Climate Change with Machine Learning https://www.climatechange.ai/papers/iclr2023/53
Tornede, A. (2023). Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions. [Dissertation, Universität Paderborn]. https://doi.org/10.17619/UNIPB/1-1780
Tornede, A., Gehring, L., Tornede, T., Wever, M., & Hüllermeier, E. (2023). Algorithm selection on a meta level. Machine learning, 112(4), 1253-1286. https://doi.org/10.1007/s10994-022-06161-4
Tornede, T., Tornede, A., Fehring, L., Gehring, L., Graf, H., Hanselle, J., Mohr, F., & Wever, M. (2023). PyExperimenter: Easily distribute experiments and track results. Journal of Open Source Software, 8(84). https://doi.org/10.48550/arXiv.2301.06348, https://doi.org/10.21105/joss.05149
Tornede, T., Tornede, A., Hanselle, J., Mohr, F., Wever, M., & Hüllermeier, E. (2023). Towards Green Automated Machine Learning: Status Quo and Future Directions. Journal of Artificial Intelligence Research, 77, 427-457. https://doi.org/10.1613/jair.1.14340
Vermetten, D., Krejca, M. S., Lindauer, M., López-Ibáñez, M., & Malan, K. M. (2023). Synergizing Theory and Practice of Automated Algorithm Design for Optimization (Dagstuhl Seminar 23332). Dagstuhl Reports, 13(8). https://doi.org/10.4230/DagRep.13.8.46
Ziegenbein, T., Syed, S., Lange, F., Potthast, M., & Wachsmuth, H. (2023). Modeling Appropriate Language in Argumentation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (S. 4344-4363). (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.238
