Zeige Ergebnisse 211 - 249 von 249
2017
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. Vorabveröffentlichung online. 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. Vorabveröffentlichung online. 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
Wachsmuth, H., Trenkmann, M., Stein, B., Engels, G., & Palakarska, T. (2014). A Review Corpus for Argumentation Analysis. In A. Gelbukh (Hrsg.), Computational Linguistics and Intelligent Text Processing - Part 2: 15th International Conference, CICLing 2014, Proceedings (S. 115-127). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 8404 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-54903-8_10
Wachsmuth, H., Trenkmann, M., Stein, B., & Engels, G. (2014). Modeling Review Argumentation for Robust Sentiment Analysis. In J. Tsujii, & J. Hajic (Hrsg.), Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers (S. 553-564). Association for Computational Linguistics (ACL). https://aclanthology.org/C14-1053
2013
Gebser, M., Jost, H., Kaminski, R., Obermeier, P., Sabuncu, O., Schaub, T., & Schneider, M. (2013). Ricochet Robots: A Transverse ASP Benchmark. In LPNMR (S. 348-360) https://doi.org/10.1007/978-3-642-40564-8_35
Hoos, H. H., Kaufmann, B., Schaub, T., & Schneider, M. (2013). Robust Benchmark Set Selection for Boolean Constraint Solvers. In LION (S. 138-152) https://doi.org/10.1007/978-3-642-44973-4_16
Wachsmuth, H., Rose, M., & Engels, G. (2013). Automatic pipeline construction for real-time annotation. In A. Gelbukh (Hrsg.), Computational Linguistics and Intelligent Text Processing: 14th International Conference, CICLing 2013, Proceedings (S. 38-49). (Lecture Notes in Computer Science; Band 7816). Springer. https://doi.org/10.1007/978-3-642-37247-6_4
Wachsmuth, H., Stein, B., & Engels, G. (2013). Information Extraction as a Filtering Task. In CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management (S. 2049-2058). Association for Computing Machinery (ACM). https://doi.org/10.1145/2505515.2505557
Wachsmuth, H., Stein, B., & Engels, G. (2013). Learning Efficient Information Extraction on Heterogeneous Texts. In R. Mitkov, & J. C. Park (Hrsg.), Proceedings of the Sixth International Joint Conference on Natural Language Processing (S. 534-542). Asian Federation of Natural Language Processing. https://aclanthology.org/I13-1061
2012
Hoos, H. H., Kaminski, R., Schaub, T., & Schneider, M. (2012). aspeed: ASP-based Solver Scheduling. In ICLP (Technical Communications) (S. 176-187) https://doi.org/10.4230/LIPIcs.ICLP.2012.176
Schneider, M., & Hoos, H. H. (2012). Quantifying Homogeneity of Instance Sets for Algorithm Configuration. In LION (S. 190-204) https://doi.org/10.1007/978-3-642-34413-8_14
Silverthorn, B., Lierler, Y., & Schneider, M. (2012). Surviving Solver Sensitivity: An ASP Practitioner's Guide. In ICLP (Technical Communications) (S. 164-175) https://doi.org/10.4230/LIPIcs.ICLP.2012.164
Wachsmuth, H., & Stein, B. (2012). Optimal Scheduling of Information Extraction Algorithms. In M. Kay, & C. Boitet (Hrsg.), Proceedings of COLING 2012: Posters (S. 1281-1290). Association for Computational Linguistics (ACL). https://aclanthology.org/C12-2125/
2011
Gebser, M., Kaufmann, B., Kaminski, R., Ostrowski, M., Schaub, T., & Schneider, M. (2011). Potassco: The Potsdam Answer Set Solving Collection. AI Commun., 24(2), 107-124. Artikel 2. https://doi.org/10.3233/AIC-2011-0491
Möller, M., Schneider, M., Wegner, M., & Schaub, T. (2011). Centurio, a General Game Player: Parallel, Java- and ASP-based. Künstliche Intell., 25(1), 17-24. Artikel 1. https://doi.org/10.1007/s13218-010-0077-4
Wachsmuth, H., & Bujna, K. (2011). Back to the Roots of Genres: Text Classification by Language Function. In H. Wang, & D. Yarowsky (Hrsg.), Proceedings of the 5th International Joint Conference on Natural Language Processing (S. 632-640). Association for Computational Linguistics (ACL). https://aclanthology.org/I11-1071.pdf
Wachsmuth, H., Stein, B., & Engels, G. (2011). Constructing Efficient Information Extraction Pipelines. In CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management (S. 2237-2240). Association for Computing Machinery (ACM). https://doi.org/10.1145/2063576.2063935
2010
Wachsmuth, H., Prettenhofer, P., & Stein, B. (2010). Efficient Statement Identification for Automatic Market Forecasting. In C-R. Huang, & D. Jurafsky (Hrsg.), Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010) (S. 1128-1136). Association for Computational Linguistics (ACL). https://aclanthology.org/C10-1127
2007
Arens, S., Buss, A., Deck, H., Dynia, M., Fischer, M., Hagedorn, H., Isaak, P., Krieger, A., Kutylowski, J., Auf Der Heide, F. M., Nesterow, V., Ogierman, A., Schrieb, J., Stobbe, B., Storm, T., & Wachsmuth, H. (2007). Smart Teams: Simulating Large Robotic Swarms in Vast Environments. In Proceedings of the 4th International Symposium on Autonomous Minirobots for Research and Edutainment (S. 215-222) https://webis.de/downloads/publications/papers/arens_2007.pdf
