2019
Biedenkapp, A., Bozkurt, H. F., Hutter, F., & Lindauer, M. (2019). Towards White-box Benchmarks for Algorithm Control.
Eggensperger, K., Lindauer, M., & Hutter, F. (2019). Pitfalls and Best Practices in Algorithm Configuration. Journal of Artificial Intelligence Research, 64, 861-893.
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).
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.
Lindauer, M. T. (2019). Automated Algorithm Selection –Predict which algorithm to use!.
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.
Lindauer, M. T. (2019). Hands-On Automated Machine Learning Tools: Auto-Sklearn and Auto-PyTorch.
Lindauer, M., van Rijn, J. N., & Kotthoff, L. (2019). The algorithm selection competitions 2015 and 2017. Artificial intelligence, 272, 86-100.
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
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
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
Potthast, M., Gienapp, L., Euchner, F., Heilenkötter, N., Weidmann, N., Wachsmuth, H., Stein, B., & Hagen, M. (2019). Argument Search: Assessing Argument Relevance. in SIGIR 2019: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (S. 1117-1120). Association for Computing Machinery, Inc.
Skitalinskaya, G., Klaff, J., & Spliethöver, M. (2019). CLEF ProtestNews Lab 2019: Contextualized word embeddings for event sentence detection and event extraction. CEUR Workshop Proceedings, 2380.
Spliethöver, M., Klaff, J., & Heuer, H. (2019). Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit Segmentation. in B. Stein, & H. Wachsmuth (Hrsg.), Proceedings of the 6th Workshop on Argument Mining (S. 74-82). Association for Computational Linguistics.
Stein, B., & Wachsmuth, H. (2019). Introduction. in B. Stein, & H. Wachsmuth (Hrsg.), Proceedings of the 6th Workshop on Argument Mining Association for Computational Linguistics (ACL).
Stein, B., & Wachsmuth, H. (Hrsg.) (2019). Proceedings of the 6th Workshop on Argument Mining. Association for Computational Linguistics (ACL).
Tornede, A., Wever, M., & Hüllermeier, E. (2019). Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. in 29th Workshop Computational Intelligence
ris.uni-paderborn.de/download/15011/17060/ci_workshop_tornede.pdf
Tornede, T., Tornede, A., Wever, M., Mohr, F., & Hüllermeier, E. (2019). AutoML for Predictive Maintenance: One Tool to RUL them all. in IoT Streams 2020: IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning
Wachsmuth, H. (2019). Argumentation Mining. By Manfred Stede and Jodi Schneider (University of Potsdam, University of Illinois at Urbana-Champaign). Morgan & Claypool (Synthesis Lectures on Human Language Technologies, edited by Graeme Hirst, volume 40), 2018, xvi+175 pp; paperback, ISBN 978-1-68173-459-0; ebook, ISBN 978-1-68173-460-6; doi:10.2200/S00883ED1V01Y201811HLT040: Argumentation Mining. Computational Linguistics, 45(3).
Wever, M., Mohr, F., Tornede, A., & Hüllermeier, E. (2019). Automating Multi-Label Classification Extending ML-Plan. in ICML 2019 Workshop AutoML
ris.uni-paderborn.de/download/10232/13177/Automating_MultiLabel_Classification_Extending_ML-Plan.pdf
2018
Ajjour, Y., Wachsmuth, H., Kiesel, D., Riehmann, P., Fan, F., Castiglia, G., Adejoh, R., Fröhlich, B., & Stein, B. (2018). Visualization of the Topic Space of Argument Search Results in args.me. in E. Blanco, & W. Lu (Hrsg.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (System Demonstrations) (S. 60-65). Association for Computational Linguistics (ACL).
Al-Khatib, K., Wachsmuth, H., Lang, K., Herpel, J., Hagen, M., & Stein, B. (2018). Modeling Deliberative Argumentation Strategies on Wikipedia. in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Long Papers) (S. 2545-2555). Association for Computational Linguistics (ACL).
Biedenkapp, A., Marben, J., Lindauer, M., & Hutter, F. (2018). CAVE: Configuration Assessment, Visualization and Evaluation. in P. M. Pardalos, R. Battiti, M. Brunato, & I. Kotsireas (Hrsg.), Learning and Intelligent Optimization (S. 115-130). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 11353 LNCS). Springer Verlag.
Bonfert, M., Spliethöver, M., Arzaroli, R., Lange, M., Hanci, M., & Porzel, R. (2018). If You Ask Nicely: A Digital Assistant Rebuking Impolite Voice Commands. in Proceedings of the 20th ACM International Conference on Multimodal Interaction (S. 95-102). Association for Computing Machinery (ACM).
Chen, W. F., Wachsmuth, H., Al-Khatib, K., & Stein, B. (2018). Learning to Flip the Bias of News Headlines. in Proceedings of the 11th International Conference on Natural Language Generation (S. 79-88). Association for Computational Linguistics (ACL).
Eggensperger, K., Lindauer, M., Hoos, H. H., Hutter, F., & Leyton-Brown, K. (2018). Efficient benchmarking of algorithm configurators via model-based surrogates. Machine learning, 107(1), 15-41.
Eggensperger, K., Lindauer, M., & Hutter, F. (2018). Neural Networks for Predicting Algorithm Runtime Distributions. in J. Lang (Hrsg.), Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (S. 1442-1448). AAAI Press/International Joint Conferences on Artificial Intelligence.
El Baff, R., Wachsmuth, H., Al-Khatib, K., & Stein, B. (2018). Challenge or Empower: Revisiting Argumentation Quality in a News Editorial Corpus. in Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018) (S. 454-464). Association for Computational Linguistics (ACL).
Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M. T., & Hutter, F. (2018). Practical Automated Machine Learning for the AutoML Challenge 2018.
www.tnt.uni-hannover.de/papers/data/1407/18-AUTOML-AutoChallenge.pdf
Habernal, I., Wachsmuth, H., Gurevych, I., & Stein, B. (2018). Before Name-calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation. in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (S. 386-396). Association for Computational Linguistics (ACL).
Habernal, I., Wachsmuth, H., Gurevych, I., & Stein, B. (2018). SemEval-2018 Task 12: The Argument Reasoning Comprehension Task. in M. Apidianaki, M. Apidianaki, S. M. Mohammad, J. May, E. Shutova, S. Bethard, & M. Carpuat (Hrsg.), Proceedings of the 12th International Workshop on Semantic Evaluation (S. 763-772). Association for Computational Linguistics (ACL).
Habernal, I., Wachsmuth, H., Gurevych, I., & Stein, B. (2018). The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants. in M. Walker, H. Ji, & A. Stent (Hrsg.), Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (S. 1930-1940). Association for Computational Linguistics (ACL).
doi.org/10.48550/arXiv.1708.01425
,Kiesel, D., Riehmann, P., Fan, F., Ajjour, Y., Wachsmuth, H., Stein, B., & Fröhlich, B. (2018). Improving Barycentric Embeddings of Topics Spaces. in IEEE VIS 2018 IEEE.
Lindauer, M., Hoos, H., Hutter, F., & Leyton-Brown, K. (2018). Selection and Configuration of Parallel Portfolios. in Handbook of Parallel Constraint Reasoning (S. 583-615). Springer International Publishing AG.
Lindauer, M. T., van Rijn, J. N., & Kotthoff, L. (2018). The Algorithm Selection Competition Series 2015-17.
Lindauer, M., & Hutter, F. (2018). Warmstarting of Model-Based Algorithm Configuration. in 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (S. 1355-1362). (Proceedings of the AAAI Conference on Artificial Intelligence). AAAI Press/International Joint Conferences on Artificial Intelligence.
Wachsmuth, H., Stede, M., El Baff, R., Al-Khatib, K., Skeppstedt, M., & Stein, B. (2018). Argumentation Synthesis following Rhetorical Strategies. in E. M. Bender, L. Derczynski, & P. Isabelle (Hrsg.), Proceedings of the 27th International Conference on Computational Linguistics (S. 3753-3765). Association for Computational Linguistics (ACL).
Wachsmuth, H., Syed, S., & Stein, B. (2018). Retrieval of the Best Counterargument without Prior Topic Knowledge. in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (S. 241-251). Association for Computational Linguistics (ACL).
Wagner, M., Lindauer, M., Mısır, M., Nallaperuma, S., & Hutter, F. (2018). A case study of algorithm selection for the traveling thief problem. Journal of heuristics, 24(3), 295-320.
2017
Ajjour, Y., Chen, W. F., Kiesel, J., Wachsmuth, H., & Stein, B. (2017). Unit Segmentation of Argumentative Texts. in I. Habernal, I. Gurevych, K. Ashley, C. Cardie, N. Green, D. Litman, G. Petasis, C. Reed, N. Slonim, & V. Walker (Hrsg.), Proceedings of the 4th Workshop on Argument Mining (S. 118-128). Association for Computational Linguistics (ACL).
Al-Khatib, K., Wachsmuth, H., Hagen, M., & Stein, B. (2017). Patterns of Argumentation Strategies across Topics. in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (S. 1351-1357). Association for Computational Linguistics (ACL).
Biedenkapp, A., Lindauer, M., Eggensperger, K., Hutter, F., Fawcett, C., & Hoos, H. H. (2017). Efficient Parameter Importance Analysis via Ablation with Surrogates.
