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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
Benjamins, C., Eimer, T., Schubert, F. G., Mohan, A., Döhler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (Angenommen/Im Druck). Contextualize Me -- The Case for Context in Reinforcement Learning. Beitrag in European Workshop on Reinforcement Learning 2023, Brüssel.
Benjamins, C., Eimer, T., Schubert, F. G., Mohan, A., Döhler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (Angenommen/Im Druck). Contextualize Me – The Case for Context in Reinforcement Learning. Transactions on Machine Learning Research. https://doi.org/10.48550/arXiv.2202.04500
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. (Angenommen/Im Druck). 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), [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). Hyperparameters in Reinforcement Learning and How to Tune Them. in ICML'23: Proceedings of the 40th International Conference on Machine Learning (S. 9104–9149). [366] https://doi.org/10.48550/arXiv.2306.01324, https://doi.org/10.5555/3618408.3618774
Eimer, T., Lindauer, M., & Raileanu, R. (Angenommen/Im Druck). Hyperparameters in Reinforcement Learning and How To Tune Them. in Proceeding of the Fortieth International Conference on Machine Learning (Proceeding of the International Conference on Machine Learning).
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
Giovanelli, J., Tornede, A., Tornede, T., & Lindauer, M. (2023). Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning.
Hanselle, J., Hüllermeier, E., Mohr, F., Ngomo, A. C. N., Sherif, M. A., Tornede, A., & Wever, M. (2023). Configuration and Evaluation. in On-The-Fly Computing -- Individualized IT-services in dynamic markets https://doi.org/10.5281/zenodo.8068466
Hutter, F., Fuks, L., Lindauer, M., & Awad, N. (2023). Method, device and computer program for producing a strategy for a robot. (Patent Nr. US11628562B2). https://patentimages.storage.googleapis.com/f9/b3/d5/7596bf6bb838dd/US11628562.pdf
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. 2287-2303. 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 Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts 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) https://doi.org/10.48550/arXiv.2306.12370
Mohan, A., Zhang, A., & Lindauer, M. (2023). A Patterns Framework for Incorporating Structure in Deep Reinforcement Learning. https://openreview.net/forum?id=KkKWsPLlAx&referrer=%5BAuthor%20Console%5D(%2Fgroup%3Fid%3DEWRL%2F2023%2FWorkshop%2FAuthors%23your-submissions)
Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A., & Lindauer, M. (Angenommen/Im Druck). AutoRL Hyperparameter Landscapes. in Second International Conference on Automated Machine Learning PMLR. https://doi.org/10.48550/arXiv.2304.02396
Mohan, A., Zhang, A., & Lindauer, M. (2023). Structure in Reinforcement Learning: A Survey and Open Problems. (Journal of Artificial Intelligence Research).
Neutatz, F., Lindauer, M., & Abedjan, Z. (2023). AutoML in Heavily Constrained Applications. VLDB Journal. https://doi.org/10.48550/arXiv.2306.16913, https://doi.org/10.1007/s00778-023-00820-1
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. https://openreview.net/forum?id=5aYGXxByI6
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. https://doi.org/10.48550/arXiv.2205.11357
Segel, S., Graf, H., Tornede, A., Bischl, B., & Lindauer, M. (Angenommen/Im Druck). Symbolic Explanations for Hyperparameter Optimization. in AutoML Conference 2023 PMLR. https://doi.org/10.5281/zenodo.8123425
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. 134-152. https://doi.org/10.48550/arXiv.2212.08913
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
Syed, S., Ziegenbein, T., Heinisch, P., Wachsmuth, H., & Potthast, M. (2023). Frame-oriented Summarization of Argumentative Discussions. 114-129. https://aclanthology.org/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, A., Deng, D., Eimer, T., Giovanelli, J., Mohan, A., Ruhkopf, T., Segel, S., Theodorakopoulos, D., Tornede, T., Wachsmuth, H., & Lindauer, M. (2023). AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. https://doi.org/10.48550/arXiv.2306.08107
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. https://doi.org/10.21105/joss.05149
Tornede, T., Tornede, A., Hanselle, J., Wever, M., Mohr, F., & 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
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
Zoeller, M., Mauthe, F., Zeiler, P., Lindauer, M., & Huber, M. (2023). Automated Machine Learning for Remaining Useful Life Predictions. in Proceedings of the international conference on Systems Science and Engineering, Human-Machine Systems, and Cybernetics (IEEE SMC) IEEE Xplore Digital Library. https://arxiv.org/abs/2306.12215
2022
Adriaensen, S., Biedenkapp, A., Shala, G., Awad, N., Eimer, T., Lindauer, M., & Hutter, F. (2022). Automated Dynamic Algorithm Configuration. Journal of Artificial Intelligence Research, 75, 1633-1699. https://doi.org/10.48550/arXiv.2205.13881, https://doi.org/10.1613/jair.1.13922
Alshomary, M., & Stahl, M. (2022). Argument Novelty and Validity Assessment via Multitask and Transfer Learning. in Proceedings of the 9th Workshop on Argument Mining (S. 111-114) https://aclanthology.org/2022.argmining-1.10.pdf
Alshomary, M., Rieskamp, J., & Wachsmuth, H. (2022). Generating Contrastive Snippets for Argument Search. in F. Toni, S. Polberg, R. Booth, M. Caminada, & H. Kido (Hrsg.), Computational Models of Argument: Proceedings of COMMA 2022 (S. 21-31). (Frontiers in Artificial Intelligence and Applications; Band 353). IOS Press. https://doi.org/10.3233/FAIA220138
Alshomary, M., El Baff, R., Gurcke, T., & Wachsmuth, H. (2022). The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments. in S. Muresan, P. Nakov, & A. Villavicencio (Hrsg.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers (S. 8782 - 8797). (Proceedings of the Annual Meeting of the Association for Computational Linguistics; Band 1). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2203.14563, https://doi.org/10.18653/v1/2022.acl-long.601
