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2021
Hutter, F., Fuks, L., Lindauer, M., & Awad, N. (2021). METHOD, DEVICE AND COMPUTER PROGRAM FOR PRODUCING A STRATEGY FOR A ROBOT. (Patent Nr. US2021008718).
Hutter, F., Fuks, L., Lindauer, M., & Awad, N. (2021). Verfahren, Vorrichtung und Computerprogramm zum Erstellen einer Strategie für einen Roboter. (Patent Nr. 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) Vorabveröffentlichung online. 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 Artikel 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. Artikel 9222553. https://doi.org/10.1109/TVCG.2020.3030425
Lindauer, M., Hutter, F., Burkart, M., & Zimmer, L. (2021). Verfahren, Vorrichtung und Computerprogramm zum Erstellen eines künstlichen neuronalen Netzes. (Patent Nr. DE102019214625).
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. Artikel 9415128. https://doi.org/10.48550/arXiv.2201.03801, https://doi.org/10.1109/TPAMI.2021.3075372
Mohr, F., Wever, M., Tornede, A., & Hullermeier, 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. Artikel 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) Vorabveröffentlichung online. 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 (S. 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. Artikel 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. Vorabveröffentlichung online. 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 (S. 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 (Hrsg.), Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Proceedings (Band 3, S. 265-296). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Band 12977). Springer Nature Switzerland AG. 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 Vorabveröffentlichung online. 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 S. Biundo, M. Do, R. Goldman, M. Katz, Q. Yang, & H. H. Zhuo (Hrsg.), Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS) (S. 597-605). (Proceedings International Conference on Automated Planning and Scheduling, ICAPS; Band 2021-August). https://doi.org/10.1609/icaps.v31i1.16008
Spitz, J., Biedenkapp, A., Speck, D., Hutter, F., Lindauer, M., & Mattmueller, R. (2021). DEVICE AND METHOD FOR PLANNING AN OPERATION OF A TECHNICAL SYSTEM. (Patent Nr. US2021383245).
Spitz, J., Biedenkapp, A., Speck, D., Hutter, F., Lindauer, M., & Mattmueller, R. (2021). Vorrichtung und Verfahren zur Planung eines Betriebs eines technischen Systems. (Patent Nr. DE102020207114).
Spliethöver, M., & Wachsmuth, H. (2021). Bias Silhouette Analysis: Towards Assessing the Quality of Bias Metrics for Word Embedding Models. In Z.-H. Zhou (Hrsg.), Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021 (S. 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. In Same Side Stance Classification Shared Task 2019: Proceedings of the Same Side Stance Classification Shared Task organized as a part of the 6th Workshop on Argument Mining (ArgMining 2019) and co-located with the the 57th Annual Meeting of the Association for Computational Linguistics (ACL19) (S. 1-7). (CEUR Workshop Proceedings; Band 2921). 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 (Hrsg.), Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (S. 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 (Hrsg.), 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 Aufl., S. 106–118). (Communications in Computer and Information Science; Band 1325). Springer Science and Business Media Deutschland GmbH. 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 GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference (S. 368-376). (ACM Conferences). Association for Computing Machinery, Inc. https://doi.org/10.1145/3449639.3459395
Wever, M., Tornede, A., Mohr, F., & Hullermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3037-3054. Artikel 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. Artikel 9382913. 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 (S. 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 (S. 1969-1972). Association for Computing Machinery, Inc. https://doi.org/10.1145/3397271.3401186
Alshomary, M., Syed, S., Potthast, M., & Wachsmuth, H. (2020). Target Inference in Argument Conclusion Generation. In D. Jurafsky, J. Chai, N. Schluter, & J. Tetreault (Hrsg.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (S. 4334-4345). (Proceedings of the Annual Meeting of the Association for Computational Linguistics). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.399
Awad, N., Shala, G., Deng, D., Mallik, N., Feurer, M., Eggensperger, K., Biedenkapp, A., Vermetten, D., Wang, H., Doerr, C., Lindauer, M., & Hutter, F. (2020). Squirrel: A Switching Hyperparameter Optimizer. Vorabveröffentlichung online. https://arxiv.org/abs/2012.08180
Biedenkapp, A., Bozkurt, H. F., Eimer, T., Hutter, F., & Lindauer, M. T. (2020). Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework. In G. De Giacomo, A. Catala, B. Dilkina, M. Milano, S. Barro, A. Bugarin, & J. Lang (Hrsg.), ECAI 2020 - 24th European Conference on Artificial Intelligence (S. 427-434). (Frontiers in Artificial Intelligence and Applications; Band 325). https://doi.org/10.3233/FAIA200122
Biedenkapp, A., Rajan, R., Hutter, F., & Lindauer, M. T. (2020). Towards TempoRL Learning When to Act. Beitrag in ICML 2020 Inductive biases, invariances and generalization in RL workshop. https://www.tnt.uni-hannover.de/papers/data/1455/20-BIG-TempoRL.pdf
Bondarenko, A., Fröbe, M., Beloucif, M., Gienapp, L., Ajjour, Y., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M., & Hagen, M. (2020). Overview of Touché 2020: Argument Retrieval: Extended Abstract. In A. Arampatzis, E. Kanoulas, T. Tsikrika, S. Vrochidis, H. Joho, C. Lioma, C. Eickhoff, A. Névéol, A. Névéol, L. Cappellato, & N. Ferro (Hrsg.), Experimental IR Meets Multilinguality, Multimodality, and Interaction: 11th International Conference of the CLEF Association, CLEF 2020, Proceedings (S. 384-395). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12260 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58219-7_26
Bondarenko, A., Fröbe, M., Beloucif, M., Gienapp, L., Ajjour, Y., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M., & Hagen, M. (2020). Overview of Touché 2020: Argument Retrieval. In CLEF 2020 Working Notes: Working Notes of CLEF 2020 - Conference and Labs of the Evaluation Forum (CEUR Workshop Proceedings; Band 2696). https://ceur-ws.org/Vol-2696/paper_261.pdf
Bondarenko, A., Hagen, M., Potthast, M., Wachsmuth, H., Beloucif, M., Biemann, C., Panchenko, A., & Stein, B. (2020). Touché: First Shared Task on Argument Retrieval. In J. M. Jose, E. Yilmaz, J. Magalhães, F. Martins, P. Castells, N. Ferro, & M. J. Silva (Hrsg.), Advances in Information Retrieval: 42nd European Conference on IR Research, ECIR 2020 (S. 517-523). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12036 LNCS). Springer. https://doi.org/10.1007/978-3-030-45442-5_67
Chen, W.-F., Al-Khatib, K., Wachsmuth, H., & Stein, B. (2020). Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity. In D. Bamman, D. Hovy, D. Jurgens, B. O'Connor, & S. Volkova (Hrsg.), Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science (S. 149-154). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.nlpcss-1.16
Chen, W. F., Al-Khatib, K., Stein, B., & Wachsmuth, H. (2020). Detecting Media Bias in News Articles using Gaussian Bias Distributions. In Findings of the Association for Computational Linguistics: EMNLP 2020 (S. 4290-4300). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2010.10649, https://doi.org/10.18653/v1/2020.findings-emnlp.383
da San Martino, G., Barrón-Cedeño, A., Wachsmuth, H., Petrov, R., & Nakov, P. (2020). SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles. In A. Herbelot, X. Zhu, A. Palmer, N. Schneider, J. May, & E. Shutova (Hrsg.), Proceedings of the 14th International Workshop on Semantic Evaluation (S. 1377-1414). International Committee for Computational Linguistics. https://doi.org/10.48550/arXiv.2009.02696, https://doi.org/10.18653/v1/2020.semeval-1.186
Denkena, B., Dittrich, M.-A., Lindauer, M. T., Mainka, J. M., & Stürenburg, L. K. (2020). Using AutoML to Optimize Shape Error Prediction in Milling Processes. SSRN Electronic Journal, 2020. https://doi.org/10.2139/ssrn.3724234
Dorsch, J., & Wachsmuth, H. (2020). Semi-Supervised Cleansing of Web Argument Corpora. In E. Cabrio, & S. Villata (Hrsg.), Proceedings of the 7th Workshop on Argument Mining (S. 19-29). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2011.01798
