Showing results 85 - 126 out of 249
2021
Alshomary, M., & Wachsmuth, H. (2021). Toward audience-aware argument generation. Patterns, 2(6), Article 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 Advance online publication. 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) Advance online publication. 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
Eggensperger, K., Müller, P., Mallik, N., Feurer, M., Sass, R., Awad, N., Lindauer, M., & Hutter, F. (2021). HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. In Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track) Advance online publication. https://arxiv.org/abs/2109.06716
Eimer, T., Biedenkapp, A., Reimer, M., Adriaensen, S., Hutter, F., & Lindauer, M. T. (2021). DACBench: A Benchmark Library for Dynamic Algorithm Configuration. In Z-H. Zhou (Ed.), Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21) (pp. 1668-1674). (IJCAI International Joint Conference on Artificial Intelligence). https://doi.org/10.24963/ijcai.2021/230
Eimer, T., Benjamins, C., & Lindauer, M. T. (2021). Hyperparameters in Contextual RL are Highly Situational. In International Workshop on Ecological Theory of RL (at NeurIPS) https://doi.org/10.48550/arXiv.2212.10876
Eimer, T., Biedenkapp, A., Hutter, F., & Lindauer, M. (2021). Self-Paced Context Evaluation for Contextual Reinforcement Learning. In Proceedings of the international conference on machine learning (ICML) Advance online publication. https://www.tnt.uni-hannover.de/papers/data/1454/space.pdf
Guerrero-Viu, J., Hauns, S., Izquierdo, S., Miotto, G., Schrodi, S., Biedenkapp, A., Elsken, T., Deng, D., Lindauer, M., & Hutter, F. (2021). Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization. In ICML 2021 Workshop AutoML Advance online publication. https://arxiv.org/abs/2105.01015
Gurcke, T., Alshomary, M., & Wachsmuth, H. (2021). Assessing the Sufficiency of Arguments through Conclusion Generation. In 8th Workshop on Argument Mining, ArgMining 2021 - Proceedings (pp. 67-77). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2110.13495
Hanselle, J., Tornede, A., Wever, M., & Hüllermeier, E. (2021). Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. In K. Karlapalem, H. Cheng, N. Ramakrishnan, R. K. Agrawal, P. K. Reddy, J. Srivastava, & T. Chakraborty (Eds.), Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Proceedings: PAKDD 2021: Advances in Knowledge Discovery and Data Mining (Vol. 12712, pp. 152-163). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12712 LNAI). https://doi.org/10.1007/978-3-030-75762-5_13
Hüllermeier, E., Mohr, F., Tornede, A., & Wever, M. (2021). Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. In ECML/PKDD workshop on Automating Data Science (ADS 2021) https://arxiv.org/abs/2109.04744
Hutter, F., Fuks, L., Lindauer, M., & Awad, N. (2021). Verfahren, Vorrichtung und Computerprogramm zum Erstellen einer Strategie für einen Roboter. (Patent No. DE102019210372A1). Deutsches Patent- und Markenamt (DPMA). https://worldwide.espacenet.com/patent/search?q=pn%3DCN112215363A
Kadra, A., Lindauer, M., Hutter, F., & Grabocka, J. (2021). Well-tuned Simple Nets Excel on Tabular Datasets. In Proceedings of the international conference on Advances in Neural Information Processing Systems (NeurIPS 2021) Advance online publication. https://arxiv.org/abs/2106.11189
Kiesel, J., Spina, D., Wachsmuth, H., & Stein, B. (2021). The Meant, the Said, and the Understood: Conversational Argument Search and Cognitive Biases. In Proceedings of the 3rd Conference on Conversational User Interfaces, CUI 2021 Article 20 Association for Computing Machinery (ACM). https://doi.org/10.1145/3469595.3469615
Kiesel, D., Riehmann, P., Wachsmuth, H., Stein, B., & Froehlich, B. (2021). Visual Analysis of Argumentation in Essays. IEEE Transactions on Visualization and Computer Graphics, 27(2), 1139-1148. Article 9222553. https://doi.org/10.1109/TVCG.2020.3030425
Liu, Z., Pavao, A., Xu, Z., Escalera, S., Ferreira, F., Guyon, I., Hong, S., Hutter, F., Ji, R., Junior, J. C. S. J., Li, G., Lindauer, M., Luo, Z., Madadi, M., Nierhoff, T., Niu, K., Pan, C., Stoll, D., Treguer, S., ... Zhang, Y. (2021). Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3108-3125. Article 9415128. Advance online publication. https://doi.org/10.48550/arXiv.2201.03801, https://doi.org/10.1109/TPAMI.2021.3075372
Mohr, F., Wever, M., Tornede, A., & Hüllermeier, E. (2021). Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3055-3066. Article 9347828. https://doi.org/10.1109/tpami.2021.3056950
Moosbauer, J., Herbinger, J., Casalicchio, G., Lindauer, M., & Bischl, B. (2021). Explaining Hyperparameter Optimization via Partial Dependence Plots. In Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) Advance online publication. https://arxiv.org/abs/2111.04820
Nouri, Z., Prakash, N., Gadiraju, U., & Wachsmuth, H. (2021). iClarify: A Tool to Help Requesters Iteratively Improve Task Descriptions in Crowdsourcing. In Proceedings of the Ninth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2021 AAAI Press/International Joint Conferences on Artificial Intelligence. https://www.humancomputation.com/2021/assets/wips_demos/HCOMP_2021_paper_111.pdf
Nouri, Z., Gadiraju, U., Engels, G., & Wachsmuth, H. (2021). What Is Unclear? Computational Assessment of Task Clarity in Crowdsourcing. In HT 2021 - Proceedings of the 32nd ACM Conference on Hypertext and Social Media (pp. 165-175). Association for Computing Machinery, Inc. https://doi.org/10.1145/3465336.3475109
Rohlfing, K. J., Cimiano, P., Scharlau, I., Matzner, T., Buhl, H. M., Buschmeier, H., Esposito, E., Grimminger, A., Hammer, B., Hab-Umbach, R., Horwath, I., Hullermeier, E., Kern, F., Kopp, S., Thommes, K., Ngonga Ngomo, A. C., Schulte, C., Wachsmuth, H., Wagner, P., & Wrede, B. (2021). Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems. IEEE Transactions on Cognitive and Developmental Systems, 13(3), 717-728. Article 9292993. https://doi.org/10.1109/TCDS.2020.3044366
Schubert, F., Eimer, T., Rosenhahn, B., & Lindauer, M. (2021). Automatic Risk Adaptation in Distributional Reinforcement Learning. Advance online publication. https://arxiv.org/abs/2106.06317
Skitalinskaya, G., Klaff, J., & Wachsmuth, H. (2021). Learning from revisions: Quality assessment of claims in argumentation at scale. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (pp. 1718-1729). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2101.10250, https://doi.org/10.18653/v1/2021.eacl-main.147
Souza, A., Nardi, L., Oliveira, L. B., Olukotun, K., Lindauer, M., & Hutter, F. (2021). Bayesian Optimization with a Prior for the Optimum. In N. Oliver, F. Pérez-Cruz, S. Kramer, J. Read, & J. A. Lozano (Eds.), Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Proceedings (Vol. 3, pp. 265-296). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 12977). Springer Nature Switzerland AG. Advance online publication. https://doi.org/10.1007/978-3-030-86523-8_17
Souza, A., Nardi, L., Oliveira, L. B., Olukotun, K., Lindauer, M., & Hutter, F. (2021). Prior-guided Bayesian Optimization. In Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021 Advance online publication. https://arxiv.org/pdf/2006.14608
Speck, D., Biedenkapp, A., Hutter, F., Mattmüller, R., & Lindauer, M. (2021). Learning Heuristic Selection with Dynamic Algorithm Configuration. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS) Advance online publication. https://doi.org/10.1609/icaps.v31i1.16008
Spliethöver, M., & Wachsmuth, H. (2021). Bias Silhouette Analysis: Towards Assessing the Quality of Bias Metrics for Word Embedding Models. In Z-H. Zhou (Ed.), Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021 (pp. 552-559). (IJCAI International Joint Conference on Artificial Intelligence). AAAI Press/International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2021/77
Stein, B., Ajjour, Y., El Baff, R., Al-Khatib, K., Cimiano, P., & Wachsmuth, H. (2021). Same side stance classification. CEUR Workshop Proceedings, 2921, 1-7. https://ceur-ws.org/Vol-2921/overview.pdf
Stürenburg, L., Denkena, B., Lindauer, M., & Wichmann, M. (2021). Maschinelles Lernen in der Prozessplanung. VDI-Z Integrierte Produktion, 163(11-12), 26-29. https://doi.org/10.37544/0042-1766-2021-11-12-26
Syed, S., Al-Khatib, K., Alshomary, M., Wachsmuth, H., & Potthast, M. (2021). Generating Informative Conclusions for Argumentative Texts. In C. Zong, F. Xia, W. Li, & R. Navigli (Eds.), Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (pp. 3482-3493). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2106.01064, https://doi.org/10.18653/v1/2021.findings-acl.306
Tornede, T., Tornede, A., Wever, M., Mohr, F., & Hüllermeier, E. (2021). AutoML for Predictive Maintenance: One Tool to RUL Them All. In J. Gama, S. Pashami, A. Bifet, M. Sayed-Mouchawe, H. Fröning, F. Pernkopf, G. Schiele, & M. Blott (Eds.), IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning: Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers (1 ed., pp. 106–118). (Communications in Computer and Information Science; Vol. 1325). Springer, Cham. https://doi.org/10.1007/978-3-030-66770-2_8
Tornede, T., Tornede, A., Wever, M., & Hüllermeier, E. (2021). Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 368-376). (ACM Conferences). Association for Computing Machinery (ACM). https://doi.org/10.1145/3449639.3459395
Wever, M., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3037-3054. Article 9321731. https://doi.org/10.1109/TPAMI.2021.3051276
Zimmer, L., Lindauer, M., & Hutter, F. (2021). Auto-PyTorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3079-3090. Article 9382913. Advance online publication. https://doi.org/10.1109/TPAMI.2021.3067763
2020
Alexandrovsky, D., Volkmar, G., Spliethöver, M., Finke, S., Herrlich, M., Döring, T., Smeddinck, J. D., & Malaka, R. (2020). Playful User-Generated Treatment: A Novel Game Design Approach for VR Exposure Therapy. In CHI PLAY 2020 - Proceedings of the Annual Symposium on Computer-Human Interaction in Play (pp. 32-45). Association for Computing Machinery, Inc. https://doi.org/10.1145/3410404.3414222
Al-Khatib, K., Hou, Y., Wachsmuth, H., Jochim, C., Bonin, F., & Stein, B. (2020). End-to-End Argumentation Knowledge Graph Construction. Proceedings of the AAAI Conference on Artificial Intelligence, 34(5), 7367-7374. https://doi.org/10.1609/aaai.v34i05.6231
Alshomary, M., Düsterhus, N., & Wachsmuth, H. (2020). Extractive Snippet Generation for Arguments. In SIGIR 2020: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1969-1972). Association for Computing Machinery, Inc. https://doi.org/10.1145/3397271.3401186
