Publications of the Institute

Showing results 1 - 42 out of 265

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).
Deng, D., & Lindauer, M. (2024). Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach. (ArXiv). Advance online publication. 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 No. DE102022210480).
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 Proceedings of the 38th conference on AAAI (pp. 12172-12180). (Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 38, No. 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 Advance online publication. 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
Mohan, A., Zhang, A., & Lindauer, M. (2024). Structure in Deep Reinforcement Learning: A Survey and Open Problems. Journal of Artificial Intelligence Research. Advance online publication. https://arxiv.org/abs/2306.16021
Theodorakopoulos, D., Stahl, F., & Lindauer, M. (2024). Hyperparameter Importance Analysis for Multi-Objective AutoML. Advance online publication.
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. Advance online publication. https://doi.org/10.48550/arXiv.2306.08107
Zöller, M., Lindauer, M., & Huber, M. (Accepted/in press). 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 (pp. 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 (pp. 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). Advance online publication. 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) Advance online publication. https://openreview.net/forum?id=DJgHzXv61b
Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Accepted/in press). Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. In AutoML Conference 2023 PMLR.
Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Accepted/in press). 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), Article 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) Advance online publication. 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 (pp. 9104–9149). Article 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 (pp. 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; Vol. 412). Verlagschriftenreihe des Heinz Nixdorf Instituts. https://doi.org/10.17619/UNIPB/1-1797
Hanselle, J., Hüllermeier, E., Mohr, F., Ngomo, A. C. N., Sherif, M. A., Tornede, A., & Wever, M. (2023). Configuration and Evaluation. In On-The-Fly Computing -- Individualized IT-services in dynamic markets https://doi.org/10.5281/zenodo.8068466
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 (Eds.), Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023) (pp. 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. (Accepted/in press). 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) Advance online publication. https://doi.org/10.48550/arXiv.2306.12370
Mohan, A., Zhang, A., & Lindauer, M. (Accepted/in press). 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. Advance online publication. https://doi.org/10.48550/arXiv.2304.02396
Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A., & Lindauer, M. (Accepted/in press). 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. Advance online publication. 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 (pp. 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. Advance online publication. 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. Advance online publication. https://openreview.net/forum?id=JQwAc91sg_x
Sengupta, M. (2023). Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms. In Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 4636–4659). Association for Computational Linguistics (ACL). https://aclanthology.org/2023.findings-emnlp.308/
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 (Eds.), Proceedings of the 16th International Natural Language Generation Conference (pp. 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) (pp. 15799–15816). (Proceedings of the Annual Meeting of the Association for Computational Linguistics; Vol. 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 (pp. 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 (pp. 4703-4717) https://doi.org/10.48550/arXiv.2310.18098, https://doi.org/10.18653/v1/2023.findings-emnlp.312