Showing results 43 - 84 out of 269
2023
Skitalinskaya, G., Spliethöver, M., & Wachsmuth, H. (2023). Claim Optimization in Computational Argumentation. In C. M. Keet, H.-Y. Lee, & S. Zarrieß (Eds.), Proceedings of the 16th International Natural Language Generation Conference (pp. 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) (pp. 15799–15816). (Proceedings of the Annual Meeting of the Association for Computational Linguistics; Vol. 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 (pp. 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 (pp. 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 (pp. 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. [Doctoral thesis, Paderborn University]. 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. 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
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 (pp. 4344-4363). (Proceedings of the Annual Meeting of the Association for Computational Linguistics; Vol. 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. Advance online publication. 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
Adriaenssen, S., Biedenkapp, A., Hutter, F., Shala, G., Lindauer, M., & Awad, N. (2022). METHOD AND DEVICE FOR LEARNING A STRATEGY AND FOR IMPLEMENTING THE STRATEGY. (Patent No. US2022027743).
Adriaenssen, S., Biedenkapp, A., Hutter, F., Shala, G., Lindauer, M., & Awad, N. (2022). Verfahren und Vorrichtung zum Lernen einer Strategie und Betreiben der Strategie. (Patent No. DE102020209281).
Alshomary, M., & Stahl, M. (2022). Argument Novelty and Validity Assessment via Multitask and Transfer Learning. In Proceedings of the 9th Workshop on Argument Mining (pp. 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 (Eds.), Computational Models of Argument: Proceedings of COMMA 2022 (pp. 21-31). (Frontiers in Artificial Intelligence and Applications; Vol. 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 (Eds.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers (pp. 8782 - 8797). (Proceedings of the Annual Meeting of the Association for Computational Linguistics; Vol. 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. Advance online publication. 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. In CLEF 2022 Working Notes: Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum (pp. 2867-2903). (CEUR Workshop Proceedings; Vol. 3180). 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 Advance online publication. 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 Advance online publication. 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://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. 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. Advance online publication. https://doi.org/10.48550/arXiv.2203.01717
Hutter, F., Lindauer, M., Kadra, A., & Grabocka, J. (2022). Verfahren und Vorrichtung zum Anlernen eines maschinellen Lernsystems. (Patent No. DE102020212108).
Hutter, F., Miotto, G., Lindauer, M., & Elsken, T. (2022). Verfahren und Vorrichtung zum Ermitteln von Netzkonfigurationen eines neuronalen Netzes unter Erfüllung einer Mehrzahl von Nebenbedingungen. (Patent No. DE102021109754).
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 (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. Advance online publication. 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., Zela, A., Stoll, D. O., Ferreira, F., Hutter, F., & Nierhoff, T. (2022). METHOD AND DEVICE FOR CREATING A SYSTEM FOR THE AUTOMATED CREATION OF MACHINE LEARNING SYSTEMS. (Patent No. US2022012636).
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
Lindauer, M., Zela, A., Stoll, D., Ferreira, F., Hutter, F., & Nierhoff, T. (2022). Verfahren und Vorrichtung zum Erstellen eines Systems zum automatisierten Erstellen von maschinellen Lernsystemen. (Patent No. DE102020208671).
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) Advance online publication. https://arxiv.org/abs/2206.03130
