2021
Stürenburg, L., Denkena, B., Lindauer, M., & Wichmann, M. (2021). Maschinelles Lernen in der Prozessplanung. VDI-Z Integrierte Produktion, 163(11-12), 26-29.
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).
doi.org/10.48550/arXiv.2106.01064
,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
Tornede, T., Tornede, A., Hanselle, J., Wever, M., Mohr, F., & Hüllermeier, E. (2021). Towards Green Automated Machine Learning: Status Quo and Future Directions.
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. [9321731].
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. [9382913].
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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).
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).
doi.org/10.48550/arXiv.2010.10649
,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.
doi.org/10.48550/arXiv.2009.02696
,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.
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).
Eggensperger, K., Haase, K., Müller, P., Lindauer, M., & Hutter, F. (2020). Neural Model-based Optimization with Right-Censored Observations.
Eimer, T., Biedenkapp, A., Hutter, F., & Lindauer, M. T. (2020). Towards Self-Paced Context Evaluation for Contextual Reinforcement Learning.
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).
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).
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).
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).
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.
Lindauer, M., & Hutter, F. (2020). Best Practices for Scientific Research on Neural Architecture Search. Journal of Machine Learning Research, 21.
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).
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.
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).
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).
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).
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
Tornede, A., Wever, M., & Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. (4th Workshop on Meta-Learning at NeurIPS 2020).
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).
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
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.
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.
Alshomary, M., & Wachsmuth, H. (2019). Siamese Neural Network for Same Side Stance Classification. CEUR Workshop Proceedings, 2921, 12-16.
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.
