Showing results 85 - 126 out of 233
2021
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) 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 (Ed.), Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21) (pp. 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) https://arxiv.org/abs/2106.05110
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 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 (pp. 67-77). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2110.13495
Hanselle, J., Tornede, A., Wever, M., & Hüllermeier, E. (2021). Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. In K. Karlapalem, H. Cheng, N. Ramakrishnan, R. K. Agrawal, P. K. Reddy, J. Srivastava, & T. Chakraborty (Eds.), Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Proceedings: PAKDD 2021: Advances in Knowledge Discovery and Data Mining (Vol. 12712, pp. 152-163). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12712 LNAI). https://doi.org/10.1007/978-3-030-75762-5_13
Hüllermeier, E., Mohr, F., Tornede, A., & Wever, M. (2021). Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. In ECML/PKDD workshop on Automating Data Science (ADS 2021) https://arxiv.org/abs/2109.04744
Kadra, A., Lindauer, M., Hutter, F., & Grabocka, J. (2021). Well-tuned Simple Nets Excel on Tabular Datasets. In Proceedings of the international conference on Advances in Neural Information Processing Systems (NeurIPS 2021) https://arxiv.org/abs/2106.11189
Kiesel, J., Spina, D., Wachsmuth, H., & Stein, B. (2021). The Meant, the Said, and the Understood: Conversational Argument Search and Cognitive Biases. In Proceedings of the 3rd Conference on Conversational User Interfaces, CUI 2021 [20] Association for Computing Machinery (ACM). https://doi.org/10.1145/3469595.3469615
Kiesel, D., Riehmann, P., Wachsmuth, H., Stein, B., & Froehlich, B. (2021). Visual Analysis of Argumentation in Essays. IEEE Transactions on Visualization and Computer Graphics, 27(2), 1139-1148. [9222553]. https://doi.org/10.1109/TVCG.2020.3030425
Liu, Z., Pavao, A., Xu, Z., Escalera, S., Ferreira, F., Guyon, I., Hong, S., Hutter, F., Ji, R., Junior, J. C. S. J., Li, G., Lindauer, M., Luo, Z., Madadi, M., Nierhoff, T., Niu, K., Pan, C., Stoll, D., Treguer, S., ... Zhang, Y. (2021). Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3108-3125. [9415128]. https://doi.org/10.48550/arXiv.2201.03801, https://doi.org/10.1109/TPAMI.2021.3075372
Mohr, F., Wever, M., Tornede, A., & Hüllermeier, E. (2021). Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(9), 3055-3066. [9347828]. https://doi.org/10.1109/tpami.2021.3056950
Moosbauer, J., Herbinger, J., Casalicchio, G., Lindauer, M., & Bischl, B. (2021). Explaining Hyperparameter Optimization via Partial Dependence Plots. In Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) https://arxiv.org/abs/2111.04820
Nouri, Z., Prakash, N., Gadiraju, U., & Wachsmuth, H. (2021). iClarify: A Tool to Help Requesters Iteratively Improve Task Descriptions in Crowdsourcing. In Proceedings of the Ninth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2021 AAAI Press/International Joint Conferences on Artificial Intelligence. https://www.humancomputation.com/2021/assets/wips_demos/HCOMP_2021_paper_111.pdf
Nouri, Z., Gadiraju, U., Engels, G., & Wachsmuth, H. (2021). What Is Unclear? Computational Assessment of Task Clarity in Crowdsourcing. In HT 2021 - Proceedings of the 32nd ACM Conference on Hypertext and Social Media (pp. 165-175). Association for Computing Machinery, Inc. https://doi.org/10.1145/3465336.3475109
Rohlfing, K. J., Cimiano, P., Scharlau, I., Matzner, T., Buhl, H. M., Buschmeier, H., Esposito, E., Grimminger, A., Hammer, B., Hab-Umbach, R., Horwath, I., Hullermeier, E., Kern, F., Kopp, S., Thommes, K., Ngonga Ngomo, A. C., Schulte, C., Wachsmuth, H., Wagner, P., & Wrede, B. (2021). Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems. IEEE Transactions on Cognitive and Developmental Systems, 13(3), 717-728. [9292993]. https://doi.org/10.1109/TCDS.2020.3044366
Schubert, F., Eimer, T., Rosenhahn, B., & Lindauer, M. (2021). Automatic Risk Adaptation in Distributional Reinforcement Learning. https://arxiv.org/abs/2106.06317
Skitalinskaya, G., Klaff, J., & Wachsmuth, H. (2021). Learning from revisions: Quality assessment of claims in argumentation at scale. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (pp. 1718-1729). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2101.10250, https://doi.org/10.18653/v1/2021.eacl-main.147
Souza, A., Nardi, L., Oliveira, L. B., Olukotun, K., Lindauer, M., & Hutter, F. (2021). Bayesian Optimization with a Prior for the Optimum. In N. Oliver, F. Pérez-Cruz, S. Kramer, J. Read, & J. A. Lozano (Eds.), Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Proceedings (Vol. 3, pp. 265-296). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 12977). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-86523-8_17
Souza, A., Nardi, L., Oliveira, L. B., Olukotun, K., Lindauer, M., & Hutter, F. (2021). Prior-guided Bayesian Optimization. In Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021 https://arxiv.org/pdf/2006.14608
Speck, D., Biedenkapp, A., Hutter, F., Mattmüller, R., & Lindauer, M. (2021). Learning Heuristic Selection with Dynamic Algorithm Configuration. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS) https://arxiv.org/abs/2006.08246
Spliethöver, M., & Wachsmuth, H. (2021). Bias Silhouette Analysis: Towards Assessing the Quality of Bias Metrics for Word Embedding Models. In Z-H. Zhou (Ed.), Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021 (pp. 552-559). (IJCAI International Joint Conference on Artificial Intelligence). AAAI Press/International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2021/77
Stein, B., Ajjour, Y., El Baff, R., Al-Khatib, K., Cimiano, P., & Wachsmuth, H. (2021). Same side stance classification. CEUR Workshop Proceedings, 2921, 1-7. https://ceur-ws.org/Vol-2921/overview.pdf
Stürenburg, L., Denkena, B., Lindauer, M., & Wichmann, M. (2021). Maschinelles Lernen in der Prozessplanung. VDI-Z Integrierte Produktion, 163(11-12), 26-29. https://doi.org/10.37544/0042-1766-2021-11-12-26
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 (Eds.), Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (pp. 3482-3493). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2106.01064, https://doi.org/10.18653/v1/2021.findings-acl.306
Tornede, T., Tornede, A., Wever, M., Mohr, F., & Hüllermeier, E. (2021). AutoML for Predictive Maintenance: One Tool to RUL Them All. In J. Gama, S. Pashami, A. Bifet, M. Sayed-Mouchawe, H. Fröning, F. Pernkopf, G. Schiele, & M. Blott (Eds.), IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning: Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers (1 ed., pp. 106–118). (Communications in Computer and Information Science; Vol. 1325). Springer, Cham. https://doi.org/10.1007/978-3-030-66770-2_8
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 (pp. 368-376). (ACM Conferences). Association for Computing Machinery (ACM). https://doi.org/10.1145/3449639.3459395
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]. https://doi.org/10.1109/TPAMI.2021.3051276
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]. https://doi.org/10.1109/TPAMI.2021.3067763
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 (pp. 32-45). Association for Computing Machinery, Inc. https://doi.org/10.1145/3410404.3414222
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. https://doi.org/10.1609/aaai.v34i05.6231
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 (pp. 1969-1972). Association for Computing Machinery, Inc. https://doi.org/10.1145/3397271.3401186
Alshomary, M., Syed, S., Potthast, M., & Wachsmuth, H. (2020). Target Inference in Argument Conclusion Generation. In D. Jurafsky, J. Chai, N. Schluter, & J. Tetreault (Eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 4334-4345). (Proceedings of the Annual Meeting of the Association for Computational Linguistics). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.399
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. 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 (Eds.), ECAI 2020 - 24th European Conference on Artificial Intelligence (pp. 427-434). (Frontiers in Artificial Intelligence and Applications; Vol. 325). https://doi.org/10.3233/FAIA200122
Biedenkapp, A., Rajan, R., Hutter, F., & Lindauer, M. T. (2020). Towards TempoRL Learning When to Act. Paper presented at 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 (Eds.), Experimental IR Meets Multilinguality, Multimodality, and Interaction: 11th International Conference of the CLEF Association, CLEF 2020, Proceedings (pp. 384-395). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 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 (Eds.), Advances in Information Retrieval: 42nd European Conference on IR Research, ECIR 2020 (pp. 517-523). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 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 (Eds.), Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science (pp. 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 (pp. 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
