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ACM; 2019. doi:<a href=\"https://doi.org/10.1145/3323165\">10.1145/3323165</a>","mla":"Scheideler, Christian, and Petra Berenbrink, editors. <i>The 31st ACM Symposium on Parallelism in Algorithms and Architectures, SPAA 2019, Phoenix, AZ, USA, June 22-24, 2019</i>. ACM, 2019, doi:<a href=\"https://doi.org/10.1145/3323165\">10.1145/3323165</a>.","short":"C. Scheideler, P. Berenbrink, eds., The 31st ACM Symposium on Parallelism in Algorithms and Architectures, SPAA 2019, Phoenix, AZ, USA, June 22-24, 2019, ACM, 2019.","chicago":"Scheideler, Christian, and Petra Berenbrink, eds. <i>The 31st ACM Symposium on Parallelism in Algorithms and Architectures, SPAA 2019, Phoenix, AZ, USA, June 22-24, 2019</i>. ACM, 2019. <a href=\"https://doi.org/10.1145/3323165\">https://doi.org/10.1145/3323165</a>.","ieee":"C. Scheideler and P. Berenbrink, Eds., <i>The 31st ACM Symposium on Parallelism in Algorithms and Architectures, SPAA 2019, Phoenix, AZ, USA, June 22-24, 2019</i>. 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ACM. <a href=\"https://doi.org/10.1145/3323165\">https://doi.org/10.1145/3323165</a>"},"project":[{"_id":"1","name":"SFB 901"},{"_id":"2","name":"SFB 901 - Project Area A"},{"name":"SFB 901 - Subproject A1","_id":"5"}]},{"author":[{"last_name":"Gmyr","first_name":"Robert","full_name":"Gmyr, Robert"},{"full_name":"Lefevre, Jonas","last_name":"Lefevre","first_name":"Jonas"},{"id":"20792","full_name":"Scheideler, Christian","last_name":"Scheideler","first_name":"Christian"}],"year":"2019","status":"public","title":"Self-Stabilizing Metric Graphs","intvolume":"        63","date_updated":"2022-01-06T06:52:07Z","language":[{"iso":"eng"}],"_id":"14830","page":"177-199","volume":63,"user_id":"15504","doi":"10.1007/s00224-017-9823-4","citation":{"mla":"Gmyr, Robert, et al. “Self-Stabilizing Metric Graphs.” <i>Theory Comput. Syst.</i>, vol. 63, no. 2, 2019, pp. 177–99, doi:<a href=\"https://doi.org/10.1007/s00224-017-9823-4\">10.1007/s00224-017-9823-4</a>.","ama":"Gmyr R, Lefevre J, Scheideler C. Self-Stabilizing Metric Graphs. <i>Theory Comput Syst</i>. 2019;63(2):177-199. doi:<a href=\"https://doi.org/10.1007/s00224-017-9823-4\">10.1007/s00224-017-9823-4</a>","bibtex":"@article{Gmyr_Lefevre_Scheideler_2019, title={Self-Stabilizing Metric Graphs}, volume={63}, DOI={<a href=\"https://doi.org/10.1007/s00224-017-9823-4\">10.1007/s00224-017-9823-4</a>}, number={2}, journal={Theory Comput. Syst.}, author={Gmyr, Robert and Lefevre, Jonas and Scheideler, Christian}, year={2019}, pages={177–199} }","apa":"Gmyr, R., Lefevre, J., &#38; Scheideler, C. (2019). Self-Stabilizing Metric Graphs. <i>Theory Comput. Syst.</i>, <i>63</i>(2), 177–199. <a href=\"https://doi.org/10.1007/s00224-017-9823-4\">https://doi.org/10.1007/s00224-017-9823-4</a>","ieee":"R. Gmyr, J. Lefevre, and C. 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SootDiff: bytecode comparison across different Java compilers. In: <i>Proceedings of the 8th ACM SIGPLAN International Workshop on State Of the Art in Program Analysis  - SOAP 2019</i>. ; 2019. doi:<a href=\"https://doi.org/10.1145/3315568.3329966\">10.1145/3315568.3329966</a>","bibtex":"@inproceedings{Dann_Hermann_Bodden_2019, title={SootDiff: bytecode comparison across different Java compilers}, DOI={<a href=\"https://doi.org/10.1145/3315568.3329966\">10.1145/3315568.3329966</a>}, booktitle={Proceedings of the 8th ACM SIGPLAN International Workshop on State Of the Art in Program Analysis  - SOAP 2019}, author={Dann, Andreas and Hermann, Ben and Bodden, Eric}, year={2019} }","apa":"Dann, A., Hermann, B., &#38; Bodden, E. (2019). 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Bodden, in: Proceedings of the 8th ACM SIGPLAN International Workshop on State Of the Art in Program Analysis  - SOAP 2019, 2019."},"date_created":"2019-11-12T12:21:11Z","type":"conference","department":[{"_id":"76"},{"_id":"34"},{"_id":"26"}]},{"department":[{"_id":"76"},{"_id":"34"},{"_id":"26"}],"type":"conference","date_created":"2019-11-12T12:22:48Z","citation":{"apa":"Kruger, S., &#38; Hermann, B. (2019). Can an Online Service Predict Gender? On the State-of-the-Art in Gender Identification from Texts. <i>2019 IEEE/ACM 2nd International Workshop on Gender Equality in Software Engineering (GE)</i>. <a href=\"https://doi.org/10.1109/ge.2019.00012\">https://doi.org/10.1109/ge.2019.00012</a>","ieee":"S. Kruger and B. Hermann, “Can an Online Service Predict Gender? On the State-of-the-Art in Gender Identification from Texts,” 2019, doi: <a href=\"https://doi.org/10.1109/ge.2019.00012\">10.1109/ge.2019.00012</a>.","short":"S. Kruger, B. 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In: <i>2019 IEEE/ACM 2nd International Workshop on Gender Equality in Software Engineering (GE)</i>. ; 2019. doi:<a href=\"https://doi.org/10.1109/ge.2019.00012\">10.1109/ge.2019.00012</a>","bibtex":"@inproceedings{Kruger_Hermann_2019, title={Can an Online Service Predict Gender? 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On the State-of-the-Art in Gender Identification from Texts","year":"2019"},{"type":"journal_article","department":[{"_id":"34"},{"_id":"355"}],"date_created":"2019-11-15T10:11:37Z","publication":"IEEE Computational Intelligence Magazine","citation":{"mla":"Couso, Ines, et al. “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning.” <i>IEEE Computational Intelligence Magazine</i>, 2019, pp. 31–44, doi:<a href=\"https://doi.org/10.1109/mci.2018.2881642\">10.1109/mci.2018.2881642</a>.","bibtex":"@article{Couso_Borgelt_Hüllermeier_Kruse_2019, title={Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning}, DOI={<a href=\"https://doi.org/10.1109/mci.2018.2881642\">10.1109/mci.2018.2881642</a>}, journal={IEEE Computational Intelligence Magazine}, author={Couso, Ines and Borgelt, Christian and Hüllermeier, Eyke and Kruse, Rudolf}, year={2019}, pages={31–44} }","ama":"Couso I, Borgelt C, Hüllermeier E, Kruse R. 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Kruse, IEEE Computational Intelligence Magazine (2019) 31–44.","chicago":"Couso, Ines, Christian Borgelt, Eyke Hüllermeier, and Rudolf Kruse. “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning.” <i>IEEE Computational Intelligence Magazine</i>, 2019, 31–44. <a href=\"https://doi.org/10.1109/mci.2018.2881642\">https://doi.org/10.1109/mci.2018.2881642</a>."},"doi":"10.1109/mci.2018.2881642","user_id":"315","page":"31-44","language":[{"iso":"eng"}],"_id":"15001","date_updated":"2022-01-06T06:52:13Z","publication_status":"published","title":"Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning","year":"2019","status":"public","publication_identifier":{"issn":["1556-603X","1556-6048"]},"author":[{"full_name":"Couso, Ines","last_name":"Couso","first_name":"Ines"},{"full_name":"Borgelt, Christian","last_name":"Borgelt","first_name":"Christian"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129"},{"full_name":"Kruse, Rudolf","first_name":"Rudolf","last_name":"Kruse"}]},{"abstract":[{"text":"Many problem settings in machine learning are concerned with the simultaneous prediction of multiple target variables of diverse type. Amongst others, such problem settings arise in multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. These subfields of machine learning are typically studied in isolation, without highlighting or exploring important relationships. In this paper, we present a unifying view on what we call multi-target prediction (MTP) problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research.","lang":"eng"}],"publication":"Data Mining and Knowledge Discovery","issue":"2","department":[{"_id":"34"},{"_id":"355"}],"type":"journal_article","date_created":"2019-11-15T10:16:34Z","file":[{"date_created":"2020-02-28T12:43:39Z","creator":"lettmann","file_id":"16155","content_type":"application/pdf","file_name":"multi-target-prediction.pdf","file_size":837808,"access_level":"open_access","relation":"main_file","date_updated":"2020-02-28T12:45:26Z"}],"intvolume":"        33","date_updated":"2022-01-06T06:52:14Z","author":[{"last_name":"Waegeman","first_name":"Willem","full_name":"Waegeman, Willem"},{"last_name":"Dembczynski","first_name":"Krzysztof","full_name":"Dembczynski, Krzysztof"},{"id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier"}],"publication_identifier":{"issn":["1573-756X"]},"title":"Multi-target prediction: a unifying view on problems and methods","year":"2019","doi":"10.1007/s10618-018-0595-5","language":[{"iso":"eng"}],"citation":{"mla":"Waegeman, Willem, et al. “Multi-Target Prediction: A Unifying View on Problems and Methods.” <i>Data Mining and Knowledge Discovery</i>, vol. 33, no. 2, 2019, pp. 293–324, doi:<a href=\"https://doi.org/10.1007/s10618-018-0595-5\">10.1007/s10618-018-0595-5</a>.","ama":"Waegeman W, Dembczynski K, Hüllermeier E. Multi-target prediction: a unifying view on problems and methods. <i>Data Mining and Knowledge Discovery</i>. 2019;33(2):293-324. doi:<a href=\"https://doi.org/10.1007/s10618-018-0595-5\">10.1007/s10618-018-0595-5</a>","bibtex":"@article{Waegeman_Dembczynski_Hüllermeier_2019, title={Multi-target prediction: a unifying view on problems and methods}, volume={33}, DOI={<a href=\"https://doi.org/10.1007/s10618-018-0595-5\">10.1007/s10618-018-0595-5</a>}, number={2}, journal={Data Mining and Knowledge Discovery}, author={Waegeman, Willem and Dembczynski, Krzysztof and Hüllermeier, Eyke}, year={2019}, pages={293–324} }","apa":"Waegeman, W., Dembczynski, K., &#38; Hüllermeier, E. (2019). Multi-target prediction: a unifying view on problems and methods. <i>Data Mining and Knowledge Discovery</i>, <i>33</i>(2), 293–324. <a href=\"https://doi.org/10.1007/s10618-018-0595-5\">https://doi.org/10.1007/s10618-018-0595-5</a>","ieee":"W. Waegeman, K. Dembczynski, and E. Hüllermeier, “Multi-target prediction: a unifying view on problems and methods,” <i>Data Mining and Knowledge Discovery</i>, vol. 33, no. 2, pp. 293–324, 2019.","chicago":"Waegeman, Willem, Krzysztof Dembczynski, and Eyke Hüllermeier. “Multi-Target Prediction: A Unifying View on Problems and Methods.” <i>Data Mining and Knowledge Discovery</i> 33, no. 2 (2019): 293–324. <a href=\"https://doi.org/10.1007/s10618-018-0595-5\">https://doi.org/10.1007/s10618-018-0595-5</a>.","short":"W. Waegeman, K. Dembczynski, E. 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Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier, W. Waegeman, in: Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019, 2019.","apa":"Mortier, T., Wydmuch, M., Dembczynski, K., Hüllermeier, E., &#38; Waegeman, W. (2019). Set-Valued Prediction in Multi-Class Classification. In <i>Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019</i>.","ieee":"T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier, and W. Waegeman, “Set-Valued Prediction in Multi-Class Classification,” in <i>Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019</i>, 2019.","ama":"Mortier T, Wydmuch M, Dembczynski K, Hüllermeier E, Waegeman W. Set-Valued Prediction in Multi-Class Classification. In: <i>Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019</i>. ; 2019.","bibtex":"@inproceedings{Mortier_Wydmuch_Dembczynski_Hüllermeier_Waegeman_2019, title={Set-Valued Prediction in Multi-Class Classification}, booktitle={Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019}, author={Mortier, Thomas and Wydmuch, Marek and Dembczynski, Krzysztof and Hüllermeier, Eyke and Waegeman, Willem}, year={2019} }","mla":"Mortier, Thomas, et al. “Set-Valued Prediction in Multi-Class Classification.” <i>Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019</i>, 2019."},"type":"conference","department":[{"_id":"34"},{"_id":"355"}],"date_created":"2019-11-15T10:20:55Z"},{"user_id":"315","doi":"10.1007/978-3-030-33778-0_22","_id":"15004","language":[{"iso":"eng"}],"publication_status":"published","date_updated":"2022-01-06T06:52:14Z","title":"Feature Selection for Analogy-Based Learning to Rank","status":"public","year":"2019","publication_identifier":{"isbn":["9783030337773","9783030337780"],"issn":["0302-9743","1611-3349"]},"author":[{"last_name":"Ahmadi Fahandar","first_name":"Mohsen","full_name":"Ahmadi Fahandar, Mohsen","id":"59547"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129"}],"type":"book_chapter","department":[{"_id":"34"},{"_id":"355"}],"date_created":"2019-11-15T10:24:45Z","place":"Cham","publication":"Discovery Science","citation":{"chicago":"Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Feature Selection for Analogy-Based Learning to Rank.” In <i>Discovery Science</i>. 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