Publikationen des Institutes

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2020


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. CEUR Workshop Proceedings, 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
Eggensperger, K., Haase, K., Müller, P., Lindauer, M., & Hutter, F. (2020). Neural Model-based Optimization with Right-Censored Observations. Vorabveröffentlichung online. https://arxiv.org/abs/2009.13828
El Baff, R., Wachsmuth, H., Al-Khatib, K., & Stein, B. (2020). Analyzing the Persuasive Effect of Style in News Editorial Argumentation. In D. Jurafsky, J. Chai, N. Schluter, & J. Tetreault (Hrsg.), Proceedings of 58th Annual Meeting of the Association for Computational Linguistics (S. 3154-3160). (Proceedings of the Annual Meeting of the Association for Computational Linguistics). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.acl-main.287
El Baff, R., Al-Khatib, K., Stein, B., & Wachsmuth, H. (2020). Persuasiveness of News Editorials depending on Ideology and Personality. In M. Nissim, V. Patti, B. Plank, & E. Durmus (Hrsg.), Proceedings of the Third Workshop on Computational Modeling of PEople’s Opinions, PersonaLity, and Emotions in Social media (S. 29-40). Association for Computational Linguistics (ACL). https://aclanthology.org/2020.peoples-1.4
Hanselle, J., Tornede, A., Wever, M., & Hüllermeier, E. (2020). Hybrid Ranking and Regression for Algorithm Selection. In U. Schmid, D. Wolter, & F. Klügl (Hrsg.), KI 2020: Advances in Artificial Intelligence - 43rd German Conference on AI, Proceedings (Band 12325, S. 59-72). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12325 LNAI). https://doi.org/10.1007/978-3-030-58285-2_5
Heindorf, S., Scholten, Y., Wachsmuth, H., Ngonga Ngomo, A. C., & Potthast, M. (2020). CauseNet: Towards a Causality Graph Extracted from the Web. In CIKM' 2020: Proceedings of the 29th ACM International Conference on Information and Knowledge Management (S. 3023-3030). Association for Computing Machinery (ACM). https://doi.org/10.1145/3340531.3412763
Kiesel, J., Lang, K., Wachsmuth, H., Hornecker, E., & Stein, B. (2020). Investigating Expectations for Voice-based and Conversational Argument Search on the Web. In CHIIR 2020: Proceedings of the 2020 Conference on Human Information Interaction and Retrieval (S. 53-62). Association for Computing Machinery, Inc. https://doi.org/10.1145/3343413.3377978
Lindauer, M., & Hutter, F. (2020). Best Practices for Scientific Research on Neural Architecture Search. Journal of Machine Learning Research, 21. https://arxiv.org/abs/1909.02453
Nouri, Z., Wachsmuth, H., & Engels, G. (2020). Mining Crowdsourcing Problems from Discussion Forums of Workers. In D. Scott, N. Bel, & C. Zong (Hrsg.), Proceedings of the 28th International Conference on Computational Linguistics (S. 6264-6276). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.coling-main.551
Shala, G., Biedenkapp, A., Awad, N., Adriaensen, S., Lindauer, M., & Hutter, F. (2020). Learning Step-Size Adaptation in CMA-ES. In T. Bäck, M. Preuss, A. Deutz, M. Emmerich, H. Wang, C. Doerr, & H. Trautmann (Hrsg.), Parallel Problem Solving from Nature – PPSN XVI: 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I (S. 691-706). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12269). Springer. Vorabveröffentlichung online. https://doi.org/10.1007/978-3-030-58112-1_48
Spliethöver, M., & Wachsmuth, H. (2020). Argument from Old Man's View: Assessing Social Bias in Argumentation. In E. Cabrio, & S. Villata (Hrsg.), Proceedings of the 7th Workshop on Argument Mining (S. 76-87). Association for Computational Linguistics (ACL). https://aclanthology.org/2020.argmining-1.9
Syed, S., Chen, W. F., Hagen, M., Stein, B., Wachsmuth, H., & Potthast, M. (2020). Task Proposal: Abstractive Snippet Generation for Web Pages. In B. Davis, Y. Graham, J. Kelleher, & Y. Sripada (Hrsg.), Proceedings of The 13th International Conference on Natural Language Generation (S. 237-241). Association for Computational Linguistics (ACL). https://aclanthology.org/2020.inlg-1.30
Tornede, A., Wever, M., & Hüllermeier, E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. In A. Appice, G. Tsoumakas, Y. Manolopoulos, & S. Matwin (Hrsg.), Discovery Science - 23rd International Conference, DS 2020, Proceedings: DS 2020: Discovery Science (Band 12323, S. 309-324). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12323 LNAI). https://doi.org/10.1007/978-3-030-61527-7_21
Tornede, A., Wever, M., Werner, S., Mohr, F., & Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. In Proceedings of The 12th Asian Conference on Machine Learning https://proceedings.mlr.press/v129/tornede20a.html
Tornede, A., Wever, M., & Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. (4th Workshop on Meta-Learning at NeurIPS 2020). Vorabveröffentlichung online. http://arxiv.org/abs/2011.08784v1
Wachsmuth, H., & Werner, T. (2020). Intrinsic Quality Assessment of Arguments. In D. Scott, N. Bel, & C. Zong (Hrsg.), COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference (S. 6739-6745). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.coling-main.592
Wever, M., Tornede, A., Mohr, F., & Hüllermeier, E. (2020). LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. In Lecture Notes in Computer Science: IDA 2020: Advances in Intelligent Data Analysis XVIII https://link.springer.com/chapter/10.1007/978-3-030-44584-3_44

2019


Ajjour, Y., Wachsmuth, H., Kiesel, J., Potthast, M., Hagen, M., & Stein, B. (2019). Data Acquisition for Argument Search: The args.me Corpus. In C. Benzmüller, & H. Stuckenschmidt (Hrsg.), KI 2019: Advances in Artificial Intelligence: 42nd German Conference on AI, Proceedings (S. 48-59). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 11793 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-30179-8_4
Ajjour, Y., Alshomary, M., Wachsmuth, H., & Stein, B. (2019). Modeling Frames in Argumentation. In K. Inui, J. Jiang, V. Ng, & X. Wan (Hrsg.), Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (S. 2922-2932). Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1290
Alshomary, M., & Wachsmuth, H. (2019). Siamese Neural Network for Same Side Stance Classification. CEUR Workshop Proceedings, 2921, 12-16. https://www.semanticscholar.org/paper/Siamese-Neural-Network-for-Same-Side-Stance-Alshomary-Wachsmuth/291badf5e32dfada9ab2aea2005646a5c6065ecf#related-papers
Alshomary, M., Völske, M., Licht, T., Wachsmuth, H., Stein, B., Hagen, M., & Potthast, M. (2019). Wikipedia Text Reuse: Within and Without. In B. Stein, N. Fuhr, L. Azzopardi, P. Mayr, D. Hiemstra, & C. Hauff (Hrsg.), Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Proceedings (S. 747-754). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 11437 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-15712-8_49
Biedenkapp, A., Bozkurt, H. F., Hutter, F., & Lindauer, M. (2019). Towards White-box Benchmarks for Algorithm Control. Vorabveröffentlichung online. https://arxiv.org/abs/1906.07644
Eggensperger, K., Lindauer, M., & Hutter, F. (2019). Pitfalls and Best Practices in Algorithm Configuration. Journal of Artificial Intelligence Research, 64, 861-893. https://doi.org/10.1613/jair.1.11420
El Baff, R., Wachsmuth, H., Al-Khatib, K., Stede, M., & Stein, B. (2019). Computational Argumentation Synthesis as a Language Modeling Task. In Proceedings of The 12th International Conference on Natural Language Generation (S. 54-64). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/W19-8607
Fuks, L., Awad, N., Hutter, F., & Lindauer, M. (2019). An evolution strategy with progressive episode lengths for playing games. In S. Kraus (Hrsg.), Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 (S. 1234-1240). (IJCAI International Joint Conference on Artificial Intelligence). AAAI Press/International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2019/172
Lindauer, M. T. (2019). Automated Algorithm Selection –Predict which algorithm to use!. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjd94W5k9TqAhWM_KQKHVbABogQFjAAegQIARAB&url=http%3A%2F%2Fceur-ws.org%2FVol-2360%2Fpaper2Keynote.pdf&usg=AOvVaw0h4cvTGwQg-XD97fhGaytv
Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Marben, J., Müller, P., & Hutter, F. (2019). BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters. Vorabveröffentlichung online. https://arxiv.org/pdf/1908.06756
Lindauer, M. T. (2019). Hands-On Automated Machine Learning Tools: Auto-Sklearn and Auto-PyTorch. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwibqKyPlNTqAhUwMewKHR7PC0oQFjAAegQIARAB&url=https%3A%2F%2Fwww.automl.org%2Fevents%2Famir19-key-note-and-automl-hands-on%2F&usg=AOvVaw1ggAEDpu7zdnlhnRtLdGQD
Lindauer, M., van Rijn, J. N., & Kotthoff, L. (2019). The algorithm selection competitions 2015 and 2017. Artificial intelligence, 272, 86-100. Vorabveröffentlichung online. https://doi.org/10.1016/j.artint.2018.10.004
Lindauer, M., Feurer, M., Eggensperger, K., Biedenkapp, A., & Hutter, F. (2019). Towards Assessing the Impact of Bayesian Optimization’s Own Hyperparameters. In DSO Workshop at IJCAI Vorabveröffentlichung online. https://arxiv.org/abs/1908.06674
Mendoza, H., Klein, A., Feurer, M., Springenberg, J. T., Urban, M., Burkart, M., Dippel, M., Lindauer, M. T., & Hutter, F. (2019). Towards Automatically-Tuned Deep Neural Networks. In Automated Machine Learning https://doi.org/10.1007/978-3-030-05318-5_7
Mohr, F., Wever, M., Tornede, A., & Hüllermeier, E. (2019). From Automated to On-The-Fly Machine Learning. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik–Informatik für Gesellschaft https://dl.gi.de/handle/20.500.12116/24989