Details zu Publikationen

2024


Benjamins, C., Cenikj, G., Nikolikj, A., Mohan, A., Eftimov, T., & Lindauer, M. (2024). Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization. In Genetic and Evolutionary Computation Conference (GECCO) Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation (SIGEVO). Vorabveröffentlichung online.
Deng, D., & Lindauer, M. (2024). Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach. (ArXiv). Vorabveröffentlichung online. https://arxiv.org/abs/2406.05088
Eimer, T., Hutter, F., Lindauer, M., & Biedenkapp, A. (2024). Verfahren zum Trainieren eines Algorithmus des maschinellen Lernens durch ein bestärkendes Lernverfahren. (Patent Nr. DE102022210480A1). Deutsches Patent- und Markenamt (DPMA). https://worldwide.espacenet.com/patent/search/family/090246319/publication/DE102022210480A1?q=pn%3DDE102022210480A1
Faggioli, G., Dietz, L., Clarke, C. L. A., Demartini, G., Hagen, M., Hauff, C., Kando, N., Kanoulas, E., Potthast, M., Stein, B., & Wachsmuth, H. (2024). Who Determines What Is Relevant? Humans or AI? Why Not Both? A spectrum of human–artificial intelligence collaboration in assessing relevance. Communications of the ACM, 67(4), 31-34. https://doi.org/10.1145/3624730
Giovanelli, J., Tornede, A., Tornede, T., & Lindauer, M. (2024). Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning. In M. Wooldridge, J. Dy, & S. Natarajan (Hrsg.), Proceedings of the 38th conference on AAAI (S. 12172-12180). (Proceedings of the AAAI Conference on Artificial Intelligence; Band 38, Nr. 11). https://doi.org/10.48550/arXiv.2309.03581, https://doi.org/10.1609/aaai.v38i11.29106
Hennig, L., Tornede, T., & Lindauer, M. (2024). Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks. In 5th Workshop on practical ML for limited/low resource settings Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2404.01965
Lindauer, M., Karl, F., Klier, A., Moosbauer, J., Tornede, A., Müller, A., Hutter, F., Feurer, M., & Bischl, B. (2024). Position Paper: A Call to Action for a Human-Centered AutoML Paradigm. In Proceedings of the international conference on machine learning Vorabveröffentlichung online.
Mohan, A., Zhang, A., & Lindauer, M. (2024). Structure in Deep Reinforcement Learning: A Survey and Open Problems. Journal of Artificial Intelligence Research. Vorabveröffentlichung online. https://arxiv.org/abs/2306.16021
Sengupta, M., El Baff, R., Alshomary, M., & Wachsmuth, H. (2024). Analyzing the Use of Metaphors in News Editorials for Political Framing. In Analyzing the Use of Metaphors in News Editorials for Political Framing (S. 3621–3631).
Theodorakopoulos, D., Stahl, F., & Lindauer, M. (Angenommen/im Druck). Hyperparameter Importance Analysis for Multi-Objective AutoML. In Proceedings of the european conference on AI (ECAI)
Tornede, A., Deng, D., Eimer, T., Giovanelli, J., Mohan, A., Ruhkopf, T., Segel, S., Theodorakopoulos, D., Tornede, T., Wachsmuth, H., & Lindauer, M. (2024). AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. Transactions on Machine Learning Research. Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2306.08107
Zöller, M., Lindauer, M., & Huber, M. (Angenommen/im Druck). auto-sktime: Automated Time Series Forecasting. In Proceedings of the 18TH Learning and Intelligent Optimization Conference (LION) https://arxiv.org/abs/2312.08528

2023


Alshomary, M., & Wachsmuth, H. (2023). Conclusion-based Counter-Argument Generation. In EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference (S. 957-967). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2301.09911
Bäumer, F., Chen, W. F., Geierhos, M., Kersting, J., & Wachsmuth, H. (2023). Dialogue-Based Requirement Compensation and Style-Adjusted Data-To-Text Generation. In On-The-Fly Computing : Individualized IT-Services in dynamic markets (S. 65-84) https://doi.org/10.5281/zenodo.8068456
Benjamins, C., Eimer, T., Schubert, F. G., Mohan, A., Döhler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (2023). Contextualize Me – The Case for Context in Reinforcement Learning. Transactions on Machine Learning Research, 2023(6). Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2202.04500
Benjamins, C., Eimer, T., Schubert, F. G., Mohan, A., Döhler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (2023). Extended Abstract: Contextualize Me -- The Case for Context in Reinforcement Learning. In The 16th European Workshop on Reinforcement Learning (EWRL 2023) Vorabveröffentlichung online. https://openreview.net/forum?id=DJgHzXv61b
Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Angenommen/im Druck). Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. In AutoML Conference 2023 PMLR.
Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Angenommen/im Druck). Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. In GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference Companion Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation (SIGEVO).
Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Thomas, J., Ullmann, T., Becker, M., Boulesteix, A.-L., Deng, D., & Lindauer, M. (2023). Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(2), Artikel e1484. https://doi.org/10.1002/widm.1484
Denkena, B., Dittrich, M.-A., Noske, H., Lange, D., Benjamins, C., & Lindauer, M. (2023). Application of machine learning for fleet-based condition monitoring of ball screw drives in machine tools. The international journal of advanced manufacturing technology, 127(3-4), 1143-1164. https://doi.org/10.1007/s00170-023-11524-9
Eimer, T., Lindauer, M., & Raileanu, R. (2023). Extended Abstract: Hyperparameters in Reinforcement Learning and How To Tune Them. In The 16th European Workshop on Reinforcement Learning (EWRL 2023) Vorabveröffentlichung online. https://openreview.net/forum?id=N3IDYxLxgtW
Eimer, T., Lindauer, M., & Raileanu, R. (2023). Hyperparameters in Reinforcement Learning and How to Tune Them. In ICML'23: Proceedings of the 40th International Conference on Machine Learning (S. 9104–9149). Artikel 366 https://doi.org/10.48550/arXiv.2306.01324, https://doi.org/10.5555/3618408.3618774
Faggioli, G., Clarke, C. L. A., Demartini, G., Hagen, M., Hauff, C., Kando, N., Kanoulas, E., Potthast, M., Stein, B., Wachsmuth, H., & Dietz, L. (2023). Perspectives on Large Language Models for Relevance Judgment. In ICTIR '23: Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval (S. 39-50). Association for Computing Machinery, Inc. https://doi.org/10.48550/arXiv.2304.09161, https://doi.org/10.1145/3578337.3605136
Haake, C.-J., Auf Der Heide, F. M., Platzner, M., Wachsmuth, H., & Wehrheim, H. (2023). On-The-Fly Computing: Individualized IT-Services in dynamic markets. (Verlagsschriftenreihe des Heinz Nixdorf Instituts; Band 412). Verlagschriftenreihe des Heinz Nixdorf Instituts. https://doi.org/10.17619/UNIPB/1-1797
Kiesel, J., Alshomary, M., Mirzakhmedova, N., Heinrich, M., Handke, N., Wachsmuth, H., & Stein, B. (2023). SemEval-2023 Task 4: ValueEval: Identification of Human Values Behind Arguments. In A. K. Ojha, A. S. Doğruöz, G. Da San Martino, H. T. Madabushi, R. Kumar, & E. Sartori (Hrsg.), Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023) (S. 2287-2303). Association for Computational Linguistics (ACL). https://doi.org/10.18653/V1/2023.SEMEVAL-1.313
Lapesa, G., Vecchi, E. M., Villata, S., & Wachsmuth, H. (2023). Mining, Assessing, and Improving Arguments in NLP and the Social Sciences. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.eacl-tutorials.1
Loni, M., Mohan, A., Asadi, M., & Lindauer, M. (Angenommen/im Druck). Learning Activation Functions for Sparse Neural Networks. In Second International Conference on Automated Machine Learning PMLR. https://arxiv.org/abs/2305.10964
Mallik, N., Bergman, E., Hvarfner, C., Stoll, D., Janowski, M., Lindauer, M., Nardi, L., & Hutter, F. (2023). PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning. In Proceedings of the international Conference on Neural Information Processing Systems (NeurIPS) Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2306.12370
Mohan, A., Zhang, A., & Lindauer, M. (Angenommen/im Druck). A Patterns Framework for Incorporating Structure in Deep Reinforcement Learning. In The 16th European Workshop on Reinforcement Learning (EWRL 2023) https://openreview.net/forum?id=KkKWsPLlAx
Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A., & Lindauer, M. (2023). AutoRL Hyperparameter Landscapes. In Second International Conference on Automated Machine Learning PMLR. Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2304.02396
Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A., & Lindauer, M. (Angenommen/im Druck). Extended Abstract: AutoRL Hyperparameter Landscapes. In The 16th European Workshop on Reinforcement Learning (EWRL 2023) https://openreview.net/forum?id=4Zu0l5lBgc
Neutatz, F., Lindauer, M., & Abedjan, Z. (2023). AutoML in Heavily Constrained Applications. VLDB Journal. Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2306.16913, https://doi.org/10.1007/s00778-023-00820-1
Nouri, Z., Prakash, N., Gadiraju, U., & Wachsmuth, H. (2023). Supporting Requesters in Writing Clear Crowdsourcing Task Descriptions Through Computational Flaw Assessment. In IUI 2023 - Proceedings of the 28th International Conference on Intelligent User Interfaces (S. 737–749). Association for Computing Machinery (ACM). https://doi.org/10.1145/3581641.3584039
Ruhkopf, T., Mohan, A., Deng, D., Tornede, A., Hutter, F., & Lindauer, M. (2023). MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information. Transactions on Machine Learning Research. Vorabveröffentlichung online. https://openreview.net/forum?id=5aYGXxByI6
Schubert, F., Benjamins, C., Döhler, S., Rosenhahn, B., & Lindauer, M. (2023). POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. Transactions on Machine Learning Research, 2023(4). https://doi.org/10.48550/arXiv.2205.11357
Segel, S., Graf, H., Tornede, A., Bischl, B., & Lindauer, M. (2023). Symbolic Explanations for Hyperparameter Optimization. In AutoML Conference 2023 PMLR. Vorabveröffentlichung online. https://openreview.net/forum?id=JQwAc91sg_x
Shoaib, M., Kotthoff, L., Lindauer, M., & Kant, S. (2023). AutoML: advanced tool for mining multivariate plant traits. Trends in Plant Science, 28(12), 1451-1452. https://doi.org/10.1016/j.tplants.2023.09.008
Skitalinskaya, G., Spliethöver, M., & Wachsmuth, H. (2023). Claim Optimization in Computational Argumentation. In C. M. Keet, H.-Y. Lee, & S. Zarrieß (Hrsg.), Proceedings of the 16th International Natural Language Generation Conference (S. 134-152). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2212.08913, https://doi.org/10.18653/v1/2023.inlg-main.10
Skitalinskaya, G., & Wachsmuth, H. (2023). To Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (S. 15799–15816). (Proceedings of the Annual Meeting of the Association for Computational Linguistics; Band 1). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.acl-long.880
Stahl, M., & Wachsmuth, H. (2023). Identifying Feedback Types to Augment Feedback Comment Generation. In Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges (S. 31-36) https://aclanthology.org/2023.inlg-genchal.5
Stahl, M., Düsterhus, N., Chen, M.-H., & Wachsmuth, H. (2023). Mind the Gap: Automated Corpus Creation for Enthymeme Detection and Reconstruction in Learner Arguments. In Findings of the Association for Computational Linguistics: EMNLP 2023 (S. 4703-4717) https://doi.org/10.48550/arXiv.2310.18098, https://doi.org/10.18653/v1/2023.findings-emnlp.312
Syed, S., Ziegenbein, T., Heinisch, P., Wachsmuth, H., & Potthast, M. (2023). Frame-oriented Summarization of Argumentative Discussions. In Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue (S. 114-129). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.sigdial-1.10
Theodorakopoulos, D., Manß, C., Stahl, F., & Lindauer, M. (2023). Green-AutoML for Plastic Litter Detection. In Proceedings of the ICLR Workshop on Tackling Climate Change with Machine Learning https://www.climatechange.ai/papers/iclr2023/53
Vermetten, D., Krejca, M. S., Lindauer, M., López-Ibáñez, M., & Malan, K. M. (2023). Synergizing Theory and Practice of Automated Algorithm Design for Optimization (Dagstuhl Seminar 23332). Dagstuhl Reports, 13(8). https://doi.org/10.4230/DagRep.13.8.46
Ziegenbein, T., Syed, S., Lange, F., Potthast, M., & Wachsmuth, H. (2023). Modeling Appropriate Language in Argumentation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (S. 4344-4363). (Proceedings of the Annual Meeting of the Association for Computational Linguistics; Band 1). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.acl-long.238
Zoeller, M., Mauthe, F., Zeiler, P., Lindauer, M., & Huber, M. (2023). Automated Machine Learning for Remaining Useful Life Predictions. In Proceedings of the international conference on Systems Science and Engineering, Human-Machine Systems, and Cybernetics (IEEE SMC) IEEE Xplore Digital Library. Vorabveröffentlichung online. https://arxiv.org/abs/2306.12215

2022


Adriaensen, S., Biedenkapp, A., Shala, G., Awad, N., Eimer, T., Lindauer, M., & Hutter, F. (2022). Automated Dynamic Algorithm Configuration. Journal of Artificial Intelligence Research, 75, 1633-1699. https://doi.org/10.48550/arXiv.2205.13881, https://doi.org/10.1613/jair.1.13922
Adriaenssen, S., Biedenkapp, A., Hutter, F., Shala, G., Lindauer, M., & Awad, N. (2022). METHOD AND DEVICE FOR LEARNING A STRATEGY AND FOR IMPLEMENTING THE STRATEGY. (Patent Nr. US2022027743).
Adriaenssen, S., Biedenkapp, A., Hutter, F., Shala, G., Lindauer, M., & Awad, N. (2022). Verfahren und Vorrichtung zum Lernen einer Strategie und Betreiben der Strategie. (Patent Nr. DE102020209281).
Alshomary, M., Rieskamp, J., & Wachsmuth, H. (2022). Generating Contrastive Snippets for Argument Search. In F. Toni, S. Polberg, R. Booth, M. Caminada, & H. Kido (Hrsg.), Computational Models of Argument: Proceedings of COMMA 2022 (S. 21-31). (Frontiers in Artificial Intelligence and Applications; Band 353). IOS Press. https://doi.org/10.3233/FAIA220138
Alshomary, M., El Baff, R., Gurcke, T., & Wachsmuth, H. (2022). The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments. In S. Muresan, P. Nakov, & A. Villavicencio (Hrsg.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers (S. 8782 - 8797). (Proceedings of the Annual Meeting of the Association for Computational Linguistics; Band 1). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2203.14563, https://doi.org/10.18653/v1/2022.acl-long.601
Benjamins, C., Raponi, E., Jankovic, A., Blom, K. V. D., Santoni, M. L., Lindauer, M., & Doerr, C. (2022). PI is back! Switching Acquisition Functions in Bayesian Optimization. Vorabveröffentlichung online. https://arxiv.org/abs/2211.01455
Benjamins, C., Jankovic, A., Raponi, E., Blom, K. V. D., Lindauer, M., & Doerr, C. (2022). Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis. Beitrag in Workshop on Meta-Learning (MetaLearn 2022). https://openreview.net/forum?id=cmxtTF_IHd
Bondarenko, A., Fröbe, M., Kiesel, J., Syed, S., Gurcke, T., Beloucif, M., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M., & Hagen, M. (2022). Overview of Touché 2022: Argument Retrieval. In CLEF 2022 Working Notes: Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum (S. 2867-2903). (CEUR Workshop Proceedings; Band 3180). https://ceur-ws.org/Vol-3180/paper-247.pdf
Bondarenko, A., Fröbe, M., Kiesel, J., Syed, S., Gurcke, T., Beloucif, M., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M., & Hagen, M. (2022). Overview of Touché 2022: Argument Retrieval: Argument Retrieval: Extended Abstract. In M. Hagen, S. Verberne, C. Macdonald, C. Seifert, K. Balog, K. Nørvåg, & V. Setty (Hrsg.), Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022, Proceedings (Part 2 Aufl., S. 339-346). (Lecture Notes in Computer Science; Band 13186). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-99739-7_43
Bothmann, L., Strickroth, S., Casalicchio, G., Rügamer, D., Lindauer, M., Scheipl, F., & Bischl, B. (2022). Developing Open Source Educational Resources for Machine Learning and Data Science. In Teaching Machine Learning Workshop at ECML 2022 Vorabveröffentlichung online. https://arxiv.org/abs/2107.14330
Chen, W.-F., Chen, M.-H., Mudgal, G., & Wachsmuth, H. (2022). Analyzing Culture-Specific Argument Structures in Learner Essays. In G. Lapesa, J. Schneider, Y. Jo, & S. Saha (Hrsg.), Proceedings of the 9th Workshop on Argument Mining (S. 51 - 61). Association for Computational Linguistics (ACL). https://aclanthology.org/2022.argmining-1.4/
Deng, D., Karl, F., Hutter, F., Bischl, B., & Lindauer, M. (2022). Efficient Automated Deep Learning for Time Series Forecasting. In Proceedings of the European Conference on Machine Learning (ECML) https://doi.org/10.48550/arXiv.2205.05511
Deng, D., & Lindauer, M. (2022). Searching in the Forest for Local Bayesian Optimization. In ECML/PKDD workshop on Meta-learning Vorabveröffentlichung online. https://arxiv.org/abs/2111.05834
Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M. T., & Hutter, F. (2022). Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning. Journal of Machine Learning Research, 23. https://www.jmlr.org/papers/volume23/21-0992/21-0992.pdf
Hasebrook, N., Morsbach, F., Kannengießer, N., Zöller, M., Franke, J., Lindauer, M., Hutter, F., & Sunyaev, A. (2022). Practitioner Motives to Select Hyperparameter Optimization Methods. Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2203.01717
Hutter, F., Lindauer, M., Kadra, A., & Grabocka, J. (2022). Verfahren und Vorrichtung zum Anlernen eines maschinellen Lernsystems. (Patent Nr. DE102020212108).
Hutter, F., Miotto, G., Lindauer, M., & Elsken, T. (2022). Verfahren und Vorrichtung zum Ermitteln von Netzkonfigurationen eines neuronalen Netzes unter Erfüllung einer Mehrzahl von Nebenbedingungen. (Patent Nr. DE102021109754).
Hvarfner, C., Stoll, D., Souza, A. L. F., Lindauer, M., Hutter, F., & Nardi, L. (2022). π BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. In Proceedings of the International conference on Learning Representation (ICLR) https://doi.org/10.48550/arXiv.2204.11051
Kiesel, J., Alshomary, M., Handke, N., Cai, X., Wachsmuth, H., & Stein, B. (2022). Identifying the Human Values behind Arguments. In S. Muresan, P. Nakov, & A. Villavicencio (Hrsg.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers (S. 4459 - 4471). (Proceedings of the Annual Meeting of the Association for Computational Linguistics; Band 1). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.acl-long.306
Lauscher, A., Wachsmuth, H., Gurevych, I., & Glavaš, G. (2022). On the Role of Knowledge in Computational Argumentation. Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2107.00281
Lauscher, A., Wachsmuth, H., Gurevych, I., & Glavaš, G. (2022). Scientia Potentia Est—On the Role of Knowledge in Computational Argumentation. Transactions of the Association for Computational Linguistics, 10(10), 1392-1422. https://doi.org/10.1162/tacl_a_00525
Lindauer, M., Zela, A., Stoll, D. O., Ferreira, F., Hutter, F., & Nierhoff, T. (2022). METHOD AND DEVICE FOR CREATING A SYSTEM FOR THE AUTOMATED CREATION OF MACHINE LEARNING SYSTEMS. (Patent Nr. US2022012636).
Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Sass, R., & Hutter, F. (2022). SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. Journal of Machine Learning Research, 2022(23). https://arxiv.org/abs/2109.09831
Lindauer, M., Zela, A., Stoll, D., Ferreira, F., Hutter, F., & Nierhoff, T. (2022). Verfahren und Vorrichtung zum Erstellen eines Systems zum automatisierten Erstellen von maschinellen Lernsystemen. (Patent Nr. DE102020208671).
Mallik, N., Hvarfner, C., Stoll, D., Janowski, M., Bergman, E., Lindauer, M. T., Nardi, L., & Hutter, F. (2022). PriorBand: HyperBand + Human Expert Knowledge. In 2022 NeurIPS Workshop on Meta Learning (MetaLearn) https://openreview.net/forum?id=ds21dwfBBH
Mohan, A., Ruhkopf, T., & Lindauer, M. (2022). Towards Meta-learned Algorithm Selection using Implicit Fidelity Information. In ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML) Vorabveröffentlichung online. https://arxiv.org/abs/2206.03130
Moosbauer, J., Casalicchio, G., Lindauer, M., & Bischl, B. (2022). Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2206.05447
Parker-Holder, J., Rajan, R., Song, X., Biedenkapp, A., Miao, Y., Eimer, T., Zhang, B., Nguyen, V., Calandra, R., Faust, A., Hutter, F., & Lindauer, M. (2022). Automated Reinforcement Learning (AutoRL): A Survey and Open Problems. Journal of Artificial Intelligence Research, 74(74), 517-568. https://doi.org/10.48550/arXiv.2201.03916, https://doi.org/10.1613/jair.1.13596
Sengupta, M., Alshomary, M., & Wachsmuth, H. (2022). Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning. In Proceedings of the 2022 Workshop on Figurative Language Processing (S. 137-142). Association for Computational Linguistics (ACL).
Spliethöver, M., Keiff, M., & Wachsmuth, H. (2022). No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media. In Proceedings of The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022) (S. 2081-2093). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.findings-emnlp.152
Stahl, M., Spliethöver, M., & Wachsmuth, H. (2022). To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation. In Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (S. 39-51). (NLPCSS 2022 - 5th Workshop on Natural Language Processing and Computational Social Science ,NLP+CSS, Held at the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022). Association for Computational Linguistics (ACL). https://aclanthology.org/2022.nlpcss-1.6/
Wachsmuth, H., & Alshomary, M. (2022). "Mama Always Had a Way of Explaining Things So I Could Understand": A Dialogue Corpus for Learning How to Explain. In Proceedings of the 29th International Conference on Computational Linguistics (S. 344 - 354). International Committee on Computational Linguistics. https://doi.org/10.48550/arXiv.2209.02508
Wachsmuth, H., & Alshomary, M. (2022). “Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations. Proceedings - International Conference on Computational Linguistics, COLING, 29(1), 344-354.

2021


Ajjour, Y., Al-Khatib, K., Cimiano, P., El Baff, R., Ell, B., Stein, B., & Wachsmuth, H. (2021). Preface. 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) (CEUR Workshop Proceedings; Band 2921). https://ceur-ws.org/Vol-2921/xpreface.pdf
Ajjour, Y., Al-Khatib, K., Cimiano, P., Baff, R. E., Ell, B., Stein, B., & Wachsmuth, H. (Hrsg.) (2021). Same Side Stance Classification Shared Task 2019. (CEUR Workshop Proceedings; Band 2921). http://ceur-ws.org/Vol-2921/
Al-Khatib, K., Trautner, L., Wachsmuth, H., Hou, Y., & Stein, B. (2021). Employing argumentation knowledge graphs for neural argument generation. In ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference (S. 4744-4754). Association for Computational Linguistics (ACL). https://aclanthology.org/2021.acl-long.366.pdf
Alshomary, M., Chen, W. F., Gurcke, T., & Wachsmuth, H. (2021). Belief-based Generation of Argumentative Claims. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (S. 224-233). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2101.09765, https://doi.org/10.18653/v1/2021.eacl-main.17
Alshomary, M., Syed, S., Dhar, A., Potthast, M., & Wachsmuth, H. (2021). Counter-Argument Generation by Attacking Weak Premises: Counter-Argument Generation by Attacking Weak Premises. In C. Zong, F. Xia, W. Li, & R. Navigli (Hrsg.), Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (S. 1816-1827). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.findings-acl.159
Alshomary, M., Gurke, T., Syed, S., Heinisch, P., Spliethöver, M., Cimiano, P., Potthast, M., & Wachsmuth, H. (2021). Key Point Analysis via Contrastive Learning and Extractive Argument Summarization. In Proceedings of The 8th Workshop on Argument Mining, (S. 184-189). Association for Computational Linguistics (ACL). https://aclanthology.org/2021.argmining-1.19.pdf
Alshomary, M., & Wachsmuth, H. (2021). Toward audience-aware argument generation. Patterns, 2(6), Artikel 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 (S. 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 Vorabveröffentlichung online. 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) Vorabveröffentlichung online. 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 (Hrsg.), Advances in Information Retrieval: 43rd European Conference on IR Research, Proceedings (S. 574-582). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 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. In CLEF 2021 Working Notes: Proceedings of the Working Notes of CLEF 2021 - Conference and Labs of the Evaluation Forum Bucharest, Romania, September 21st to 24th, 2021. (S. 2258-2284). (CEUR Workshop Proceedings; Band 2936). 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 (Hrsg.), Experimental IR Meets Multilinguality, Multimodality, and Interaction. 12th International Conference of the CLEF Association (CLEF 2021) (Band 12880, S. 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 (Hrsg.), Findings of the Association for Computational Linguistics: EMNLP 2021 (S. 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) Vorabveröffentlichung online. 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 (Hrsg.), Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21) (S. 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) Vorabveröffentlichung online. 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 Vorabveröffentlichung online. 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 (S. 67-77). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2110.13495
Hutter, F., Fuks, L., Lindauer, M., & Awad, N. (2021). METHOD, DEVICE AND COMPUTER PROGRAM FOR PRODUCING A STRATEGY FOR A ROBOT. (Patent Nr. US2021008718).