Showing results 43 - 84 out of 233
2022
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. 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. Paper presented at 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 (Eds.), Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022, Proceedings (Part 2 ed., pp. 339-346). (Lecture Notes in Computer Science; Vol. 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 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 (Eds.), Proceedings of the 9th Workshop on Argument Mining (pp. 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 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) https://arxiv.org/abs/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. https://doi.org/10.1007/s40194-022-01339-9
Hvarfner, C., Stoll, D., Souza, A. L. F., Lindauer, M., Hutter, F., & Nardi, L. (Accepted/In press). 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 (Eds.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers (pp. 4459 - 4471). (Proceedings of the Annual Meeting of the Association for Computational Linguistics; Vol. 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. 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) https://arxiv.org/abs/2206.03130
Moosbauer, J., Casalicchio, G., Lindauer, M., & Bischl, B. (2022). Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. 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
Sass, R., Bergman, E., Biedenkapp, A., Hutter, F., & Lindauer, M. (2022). DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning. In ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML) https://arxiv.org/pdf/2206.03493v1.pdf
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 (pp. 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) (pp. 2081-2093). Association for Computational Linguistics. 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 (pp. 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 (pp. 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 (pp. 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. (Eds.) (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 (pp. 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 (pp. 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 (Eds.), Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (pp. 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, (pp. 184-189). Association for Computational Linguistics (ACL). https://aclanthology.org/2021.argmining-1.19.pdf
Alshomary, M., & Wachsmuth, H. (2021). Toward audience-aware argument generation. Patterns, 2(6), [100253]. https://doi.org/10.1016/j.patter.2021.100253
Barrow, J., Jain, R., Lipka, N., Dernoncourt, F., Morariu, V. I., Manjunatha, V., Oard, D. W., Resnik, P., & Wachsmuth, H. (2021). Syntopical graphs for computational argumentation tasks. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (pp. 1583-1595). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.acl-long.126
Benjamins, C., Eimer, T., Schubert, F., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (2021). CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. In Workshop on Ecological Theory of Reinforcement Learning, NeurIPS 2021 https://arxiv.org/abs/2110.02102
Biedenkapp, A., Rajan, R., Hutter, F., & Lindauer, M. (2021). TempoRL: Learning When to Act. In Proceedings of the international conference on machine learning (ICML) https://arxiv.org/abs/2106.05262
Bondarenko, A., Gienapp, L., Fröbe, M., Beloucif, M., Ajjour, Y., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M., & Hagen, M. (2021). Overview of Touché 2021: Argument Retrieval: Extended Abstract. In D. Hiemstra, M-F. Moens, J. Mothe, R. Perego, M. Potthast, & F. Sebastiani (Eds.), Advances in Information Retrieval: 43rd European Conference on IR Research, Proceedings (pp. 574-582). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12657 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-72240-1_67
Bondarenko, A., Gienapp, L., Fröbe, M., Beloucif, M., Ajjour, Y., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M., & Hagen, M. (2021). Overview of Touché 2021: Argument retrieval. CEUR Workshop Proceedings, 2936, 2258-2284. https://ceur-ws.org/Vol-2936/paper-205.pdf
Bondarenko, A., Gienapp, L., Fröbe, M., Beloucif, M., Ajjour, Y., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M., & Hagen, M. (2021). Overview of Touché 2021: Argument Retrieval. In K. S. Candan, B. Ionescu, L. Goeuriot, H. Müller, A. Joly, M. Maistro, F. Piroi, G. Faggioli, & N. Ferro (Eds.), Experimental IR Meets Multilinguality, Multimodality, and Interaction. 12th International Conference of the CLEF Association (CLEF 2021) (Vol. 12880, pp. 450-467). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-030-85251-1_28
Chen, W. F., Al-Khati, K., Stein, B., & Wachsmuth, H. (2021). Controlled Neural Sentence-Level Reframing of News Articles. In M-F. Moens, X. Huang, L. Specia, & S. W-T. Yih (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2021 (pp. 2683-2693). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.findings-emnlp.228
