Zeige Ergebnisse 211 - 252 von 269
2018
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. https://doi.org/10.1007/978-3-319-63516-3_15
Lindauer, M. T., van Rijn, J. N., & Kotthoff, L. (2018). The Algorithm Selection Competition Series 2015-17. Vorabveröffentlichung online. https://arxiv.org/abs/1805.01214v1
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. https://arxiv.org/abs/1709.04636v3
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). https://aclanthology.org/C18-1318
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). https://doi.org/10.18653/v1/p18-1023
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. https://doi.org/10.1007/s10732-017-9328-y
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). https://doi.org/10.18653/v1/W17-5115
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). https://doi.org/10.18653/v1/d17-1141
Biedenkapp, A., Lindauer, M., Eggensperger, K., Hutter, F., Fawcett, C., & Hoos, H. H. (2017). Efficient Parameter Importance Analysis via Ablation with Surrogates. In Proceedings of the AAAI Conference on Artificial Intelligence https://doi.org/10.1609/aaai.v31i1.10657
Hutter, F., Lindauer, M., Balint, A., Bayless, S., Hoos, H., & Leyton-Brown, K. (2017). The Configurable SAT Solver Challenge (CSSC). Artificial intelligence, 243, 1-25. https://doi.org/10.1016/j.artint.2016.09.006
Kiesel, J., Wachsmuth, H., Al-Khatib, K., & Stein, B. (2017). WAT-SL: A Customizable Web Annotation Tool for Segment Labeling. In A. Penas, & A. Martins (Hrsg.), Proceedings of the EACL 2017 Software Demonstrations (S. 13-16). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/e17-3004
Lindauer, M., Hutter, F., Hoos, H. H., & Schaub, T. (2017). AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract). In C. Sierra (Hrsg.), International Joint Conference on Artificial Intelligence (IJCAI 2017) (S. 5025-5029). AAAI Press/International Joint Conferences on Artificial Intelligence. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjo2uHc_87qAhVpzMQBHf2lDTwQFjABegQIAxAB&url=https%3A%2F%2Fwww.ijcai.org%2FProceedings%2F2017%2F0715.pdf&usg=AOvVaw1ART0bWLbCU4uLc4oV19yv
Lindauer, M. T., van Rijn, J. N., & Kotthoff, L. (2017). Open Algorithm Selection Challenge 2017 Setup and Scenarios. http://proceedings.mlr.press/v79/lindauer17a/lindauer17a.pdf
Wachsmuth, H., Naderi, N., Habernal, I., Hou, Y., Hirst, G., Gurevych, I., & Stein, B. (2017). Argumentation Quality Assessment: Theory vs. Practice. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Short Papers) (S. 250-255). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/P17-2039
Wachsmuth, H., & Stein, B. (2017). A Universal Model for Discourse-Level Argumentation Analysis. ACM Transactions on Internet Technology, 17(3), Artikel 28. https://doi.org/10.1145/2957757
Wachsmuth, H., Potthast, M., Al-Khatib, K., Ajjour, Y., Puschmann, J., Qu, J., Dorsch, J., Morari, V., Bevendorff, J., & Stein, B. (2017). Building an Argument Search Engine for the Web. 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. 49-59). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/W17-5106
Wachsmuth, H., Naderi, N., Hou, Y., Bilu, Y., Prabhakaran, V., Thijm, T. A., Hirst, G., & Stein, B. (2017). Computational Argumentation Quality Assessment in Natural Language. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers (S. 176-187). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/e17-1017
Wachsmuth, H., Stein, B., & Ajjour, Y. (2017). “PageRank” for Argument Relevance. In P. Blunsom, A. Koller, & M. Lapata (Hrsg.), Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Long Papers (S. 1117-1127). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/e17-1105
Wachsmuth, H., da San Martino, G., Kiesel, D., & Stein, B. (2017). The Impact of Modeling Overall Argumentation with Tree Kernels. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (S. 2379-2389). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d17-1253
Wagner, M., Friedrich, T., & Lindauer, M. (2017). Improving local search in a minimum vertex cover solver for classes of networks. In 2017 IEEE Congress on Evolutionary Computation (CEC): Proceedings (S. 1704-1711). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/cec.2017.7969507
2016
Al-Khatib, K., Wachsmuth, H., Kiesel, J., Hagen, M., & Stein, B. (2016). A News Editorial Corpus for Mining Argumentation Strategies. In Y. Matsumoto, & R. Prasad (Hrsg.), Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers (S. 3433-3443). Association for Computational Linguistics (ACL). https://aclanthology.org/C16-1324/
Al-Khatib, K., Wachsmuth, H., Hagen, M., Köhler, J., & Stein, B. (2016). Cross-Domain Mining of Argumentative Text through Distant Supervision. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (S. 1395-1404). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/n16-1165
Bischl, B., Kerschke, P., Kotthoff, L., Lindauer, M., Malitsky, Y., Fréchette, A., Hoos, H., Hutter, F., Leyton-Brown, K., Tierney, K., & Vanschoren, J. (2016). ASlib: A benchmark library for algorithm selection. Artificial intelligence, 237, 41-58. https://doi.org/10.1016/j.artint.2016.04.003
Lindauer, M., Bergdoll, R. D., & Hutter, F. (2016). An Empirical Study of Per-instance Algorithm Scheduling. In P. Festa, M. Sellmann, & J. Vanschoren (Hrsg.), Learning and Intelligent Optimization (S. 253-259). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 10079 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-50349-3_20
Lindauer, M., Hoos, H., Leyton-Brown, K., & Schaub, T. (2016). Automatic construction of parallel portfolios via algorithm configuration. Artificial intelligence, 244, 272-290. https://doi.org/10.1016/j.artint.2016.05.004
Manthey, N., & Lindauer, M. (2016). SpyBug: Automated Bug Detection in the Configuration Space of SAT Solvers. In D. Le Berre, & N. Creignou (Hrsg.), Theory and Applications of Satisfiability Testing – SAT 2016 (S. 554-561). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 9710). Springer Verlag. https://doi.org/10.1007/978-3-319-40970-2_36
Wachsmuth, H., Al-Khatib, K., & Stein, B. (2016). Using Argument Mining to Assess the Argumentation Quality of Essays. In Y. Matsumoto, & R. Prasad (Hrsg.), Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers (S. 1680-1691). Association for Computational Linguistics (ACL). https://aclanthology.org/C16-1158
2015
Albrecht, S. V., Beck, J. C., Buckeridge, D. L., Botea, A., Caragea, C., Chi, C. H., Damoulas, T., Dilkina, B., Eaton, E., Fazli, P., Ganzfried, S., Giles, C. L., Guillet, S., Holte, R., Hutter, F., Koch, T., Leonetti, M., Lindauer, M., Machado, M. C., ... Zheng, Y. (2015). Reports on the 2015 AAAI Workshop Series. AI magazine, 36(2), 90-101. https://doi.org/10.1609/aimag.v36i2.2590
Falkner, S., Lindauer, M., & Hutter, F. (2015). SpySMAC: Automated Configuration and Performance Analysis of SAT Solvers. In M. Heule, & S. Weaver (Hrsg.), Theory and Applications of Satisfiability Testing – SAT 2015 (S. 215-222). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 9340). Springer Verlag. https://doi.org/10.1007/978-3-319-24318-4_16
Hutter, F., Lindauer, M., & Malitsky, Y. (2015). Preface. In Algorithm configuration: papers presented at the Twenty-Ninth AAAI Conference on Artificial Intelligence (S. vii). (AAAI Workshop - Technical Report).
Lindauer, M., Hoos, H. H., Schaub, T., & Hutter, F. (2015). Auto folio: Algorithm configuration for algorithm selection. In Algorithm Configuration: papers presented at the Twenty-Ninth AAAI Conference on Artificial Intelligence (S. 9-15). (AAAI Workshop - Technical Report). AI Access Foundation.
Lindauer, M., Hoos, H. H., Hutter, F., & Schaub, T. (2015). AutoFolio: Algorithm Configuration for Algorithm Selection. In AAAI Workshop: Algorithm Configuration https://dblp.org/db/conf/aaai/aconfig2015.html#LindauerHHS15
Lindauer, M. T., Hoos, H., Hutter, F., & Schaub, T. (2015). AutoFolio: An Automatically Configured Algorithm Selector. Journal of Artificial Intelligence Research, 53, 745-778. https://doi.org/10.1613/jair.4726
Lindauer, M., Hoos, H. H., & Hutter, F. (2015). From Sequential Algorithm Selection to Parallel Portfolio Selection. In C. Dhaenens, L. Jourdan, & M.-E. Marmion (Hrsg.), Learning and Intelligent Optimization (S. 1-16). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 8994). Springer Verlag. https://doi.org/10.1007/978-3-319-19084-6_1
Wachsmuth, H., Kiesel, J., & Stein, B. (2015). Sentiment Flow – A General Model of Web Review Argumentation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (S. 601-611). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d15-1072
Wachsmuth, H. (2015). Text Analysis Pipelines: Towards Ad-hoc Large-Scale Text Mining. (Lecture Notes in Computer Science; Band 9383). Springer Verlag. https://doi.org/10.1007/978-3-319-25741-9
2014
Brüseke, F., Wachsmuth, H., Engels, G., & Becker, S. (2014). PBlaman: performance blame analysis based on Palladio contracts. Concurrency and Computation: Practice and Experience, 26(12), 1975-2004. https://doi.org/10.1002/cpe.3226
Hoos, H., Kaminski, R., Lindauer, M., & Schaub, T. (2014). aspeed: Solver scheduling via answer set programming. Theory and Practice of Logic Programming, 15(1), 117-142. https://doi.org/10.1017/s1471068414000015
Hoos, H., Lindauer, M., & Schaub, T. (2014). claspfolio 2: Advances in Algorithm Selection for Answer Set Programming. Theory and Practice of Logic Programming, 14(4-5), 569-585. https://doi.org/10.1017/S1471068414000210
Hoos, H. H., Kaminski, R., Lindauer, M., & Schaub, T. (2014). Solver Scheduling via Answer Set Programming. CoRR, abs/1401.1024. https://dblp.org/db/journals/corr/corr1401.html#HoosKLS14
Hutter, F., López-Ibáñez, M., Fawcett, C., Lindauer, M., Hoos, H. H., Leyton-Brown, K., & Stützle, T. (2014). AClib: A Benchmark Library for Algorithm Configuration. In Learning and Intelligent Optimization (S. 36-40). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 8426 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-09584-4_4
