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2023
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
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. Vorabveröffentlichung online. https://arxiv.org/abs/2306.12215
Zöller, M., Lindauer, M., & Huber, M. (2023). auto-sktime: Automated Time Series Forecasting. Vorabveröffentlichung online. https://arxiv.org/abs/2312.08528
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
Benjamins, C., Raponi, E., Jankovic, A., Blom, K. V. D., Santoni, M. L., Lindauer, M., & Doerr, C. (2022). PI is back! Switching Acquisition Functions in Bayesian Optimization. Vorabveröffentlichung online. https://arxiv.org/abs/2211.01455
Benjamins, C., Jankovic, A., Raponi, E., Blom, K. V. D., Lindauer, M., & Doerr, C. (2022). Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis. Beitrag in Workshop on Meta-Learning (MetaLearn 2022). https://openreview.net/forum?id=cmxtTF_IHd
Bondarenko, A., Fröbe, M., Kiesel, J., Syed, S., Gurcke, T., Beloucif, M., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M., & Hagen, M. (2022). Overview of Touché 2022: Argument Retrieval. CEUR Workshop Proceedings, 3180, 2867-2903. https://ceur-ws.org/Vol-3180/paper-247.pdf
Bondarenko, A., Fröbe, M., Kiesel, J., Syed, S., Gurcke, T., Beloucif, M., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M., & Hagen, M. (2022). Overview of Touché 2022: Argument Retrieval: Argument Retrieval: Extended Abstract. In M. Hagen, S. Verberne, C. Macdonald, C. Seifert, K. Balog, K. Nørvåg, & V. Setty (Hrsg.), Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022, Proceedings (Part 2 Aufl., S. 339-346). (Lecture Notes in Computer Science; Band 13186). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-99739-7_43
Bothmann, L., Strickroth, S., Casalicchio, G., Rügamer, D., Lindauer, M., Scheipl, F., & Bischl, B. (2022). Developing Open Source Educational Resources for Machine Learning and Data Science. In Teaching Machine Learning Workshop at ECML 2022 Vorabveröffentlichung online. https://arxiv.org/abs/2107.14330
Chen, W-F., Chen, M-H., Mudgal, G., & Wachsmuth, H. (2022). Analyzing Culture-Specific Argument Structures in Learner Essays. In G. Lapesa, J. Schneider, Y. Jo, & S. Saha (Hrsg.), Proceedings of the 9th Workshop on Argument Mining (S. 51 - 61). Association for Computational Linguistics (ACL). https://aclanthology.org/2022.argmining-1.4/
Deng, D., Karl, F., Hutter, F., Bischl, B., & Lindauer, M. (2022). Efficient Automated Deep Learning for Time Series Forecasting. In Proceedings of the European Conference on Machine Learning (ECML) https://doi.org/10.48550/arXiv.2205.05511
Deng, D., & Lindauer, M. (2022). Searching in the Forest for Local Bayesian Optimization. In ECML/PKDD workshop on Meta-learning Vorabveröffentlichung online. https://arxiv.org/abs/2111.05834
Fehring, L., Hanselle, J., & Tornede, A. (2022). HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection. In NeurIPS Workshop on Meta Learning (MetaLearn 2022) Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2210.17341
Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M. T., & Hutter, F. (2022). Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning. Journal of Machine Learning Research, 23. https://www.jmlr.org/papers/volume23/21-0992/21-0992.pdf
Gevers, K., Tornede, A., Wever, M., Schöppner, V., & Hüllermeier, E. (2022). A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. Welding in the world, 66(10), 2157-2170. Vorabveröffentlichung online. https://doi.org/10.1007/s40194-022-01339-9
Hasebrook, N., Morsbach, F., Kannengießer, N., Zöller, M., Franke, J., Lindauer, M., Hutter, F., & Sunyaev, A. (2022). Practitioner Motives to Select Hyperparameter Optimization Methods. Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2203.01717
Hvarfner, C., Stoll, D., Souza, A. L. F., Lindauer, M., Hutter, F., & Nardi, L. (2022). π BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. In Proceedings of the International conference on Learning Representation (ICLR) https://doi.org/10.48550/arXiv.2204.11051
Kiesel, J., Alshomary, M., Handke, N., Cai, X., Wachsmuth, H., & Stein, B. (2022). Identifying the Human Values behind 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. 4459 - 4471). (Proceedings of the Annual Meeting of the Association for Computational Linguistics; Band 1). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.acl-long.306
Lauscher, A., Wachsmuth, H., Gurevych, I., & Glavaš, G. (2022). On the Role of Knowledge in Computational Argumentation. Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2107.00281
Lauscher, A., Wachsmuth, H., Gurevych, I., & Glavaš, G. (2022). Scientia Potentia Est—On the Role of Knowledge in Computational Argumentation. Transactions of the Association for Computational Linguistics, 10(10), 1392-1422. https://doi.org/10.1162/tacl_a_00525
Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Sass, R., & Hutter, F. (2022). SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. Journal of Machine Learning Research, 2022(23). https://arxiv.org/abs/2109.09831
Mallik, N., Hvarfner, C., Stoll, D., Janowski, M., Bergman, E., Lindauer, M. T., Nardi, L., & Hutter, F. (2022). PriorBand: HyperBand + Human Expert Knowledge. In 2022 NeurIPS Workshop on Meta Learning (MetaLearn) https://openreview.net/forum?id=ds21dwfBBH
Mohan, A., Ruhkopf, T., & Lindauer, M. (2022). Towards Meta-learned Algorithm Selection using Implicit Fidelity Information. In ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML) Vorabveröffentlichung online. https://arxiv.org/abs/2206.03130
Moosbauer, J., Casalicchio, G., Lindauer, M., & Bischl, B. (2022). Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2206.05447
Parker-Holder, J., Rajan, R., Song, X., Biedenkapp, A., Miao, Y., Eimer, T., Zhang, B., Nguyen, V., Calandra, R., Faust, A., Hutter, F., & Lindauer, M. (2022). Automated Reinforcement Learning (AutoRL): A Survey and Open Problems. Journal of Artificial Intelligence Research, 74(74), 517-568. https://doi.org/10.48550/arXiv.2201.03916, https://doi.org/10.1613/jair.1.13596
Schede, E., Brandt, J., Tornede, A., Wever, M., Bengs, V., Hüllermeier, E., & Tierney, K. (2022). A Survey of Methods for Automated Algorithm Configuration. Journal of Artificial Intelligence Research, 75, 425-487. https://doi.org/10.1613/jair.1.13676
Sengupta, M., Alshomary, M., & Wachsmuth, H. (2022). Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning. In Proceedings of the 2022 Workshop on Figurative Language Processing (S. 137-142). Association for Computational Linguistics (ACL).
Spliethöver, M., Keiff, M., & Wachsmuth, H. (2022). No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media. In Proceedings of The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022) (S. 2081-2093). Association for Computational Linguistics. Vorabveröffentlichung online. https://doi.org/10.18653/v1/2022.findings-emnlp.152
Stahl, M., Spliethöver, M., & Wachsmuth, H. (2022). To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation. In Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (S. 39-51). (NLPCSS 2022 - 5th Workshop on Natural Language Processing and Computational Social Science ,NLP+CSS, Held at the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022). Association for Computational Linguistics (ACL). https://aclanthology.org/2022.nlpcss-1.6/
Tornede, A., Bengs, V., & Hüllermeier, E. (2022). Machine Learning for Online Algorithm Selection under Censored Feedback. In Proceedings of the 36th AAAI Conference on Artificial Intelligence (S. 10370-10380) https://ojs.aaai.org/index.php/AAAI/article/view/21279
Wachsmuth, H., & Alshomary, M. (2022). "Mama Always Had a Way of Explaining Things So I Could Understand": A Dialogue Corpus for Learning How to Explain. In Proceedings of the 29th International Conference on Computational Linguistics (S. 344 - 354). International Committee on Computational Linguistics. https://doi.org/10.48550/arXiv.2209.02508
Wachsmuth, H., & Alshomary, M. (2022). “Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations. Proceedings - International Conference on Computational Linguistics, COLING, 29(1), 344-354.
2021
Ajjour, Y., Al-Khatib, K., Cimiano, P., El Baff, R., Ell, B., Stein, B., & Wachsmuth, H. (2021). Preface. CEUR Workshop Proceedings, 2921. https://ceur-ws.org/Vol-2921/xpreface.pdf
Ajjour, Y., Al-Khatib, K., Cimiano, P., Baff, R. E., Ell, B., Stein, B., & Wachsmuth, H. (Hrsg.) (2021). Same Side Stance Classification Shared Task 2019. (CEUR Workshop Proceedings). http://ceur-ws.org/Vol-2921/
Al-Khatib, K., Trautner, L., Wachsmuth, H., Hou, Y., & Stein, B. (2021). Employing argumentation knowledge graphs for neural argument generation. In ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference (S. 4744-4754). Association for Computational Linguistics (ACL). https://aclanthology.org/2021.acl-long.366.pdf
Alshomary, M., Chen, W. F., Gurcke, T., & Wachsmuth, H. (2021). Belief-based Generation of Argumentative Claims. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (S. 224-233). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2101.09765, https://doi.org/10.18653/v1/2021.eacl-main.17
Alshomary, M., Syed, S., Dhar, A., Potthast, M., & Wachsmuth, H. (2021). Counter-Argument Generation by Attacking Weak Premises: Counter-Argument Generation by Attacking Weak Premises. In C. Zong, F. Xia, W. Li, & R. Navigli (Hrsg.), Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (S. 1816-1827). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.findings-acl.159
Alshomary, M., Gurke, T., Syed, S., Heinisch, P., Spliethöver, M., Cimiano, P., Potthast, M., & Wachsmuth, H. (2021). Key Point Analysis via Contrastive Learning and Extractive Argument Summarization. In Proceedings of The 8th Workshop on Argument Mining, (S. 184-189). Association for Computational Linguistics (ACL). https://aclanthology.org/2021.argmining-1.19.pdf
