[{"department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"type":"preprint","date_created":"2020-08-17T12:10:55Z","abstract":[{"lang":"eng","text":"A common statistical task lies in showing asymptotic normality of certain\nstatistics. In many of these situations, classical textbook results on weak\nconvergence theory suffice for the problem at hand. However, there are quite\nsome scenarios where stronger results are needed in order to establish an\nasymptotic normal approximation uniformly over a family of probability\nmeasures. In this note we collect some results in this direction. We restrict\nourselves to weak convergence in $\\mathbb R^d$ with continuous limit measures."}],"citation":{"mla":"Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak Convergence Theory.” <i>ArXiv:1903.09864</i>, 2019.","ama":"Bengs V, Holzmann H. Uniform approximation in classical weak convergence theory. <i>arXiv:190309864</i>. 2019.","bibtex":"@article{Bengs_Holzmann_2019, title={Uniform approximation in classical weak convergence theory}, journal={arXiv:1903.09864}, author={Bengs, Viktor and Holzmann, Hajo}, year={2019} }","apa":"Bengs, V., &#38; Holzmann, H. (2019). Uniform approximation in classical weak convergence theory. <i>ArXiv:1903.09864</i>.","ieee":"V. Bengs and H. Holzmann, “Uniform approximation in classical weak convergence theory,” <i>arXiv:1903.09864</i>. 2019.","chicago":"Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak Convergence Theory.” <i>ArXiv:1903.09864</i>, 2019.","short":"V. Bengs, H. Holzmann, ArXiv:1903.09864 (2019)."},"publication":"arXiv:1903.09864","user_id":"76599","_id":"18018","date_updated":"2022-01-06T06:53:25Z","author":[{"last_name":"Bengs","first_name":"Viktor","full_name":"Bengs, Viktor"},{"full_name":"Holzmann, Hajo","last_name":"Holzmann","first_name":"Hajo"}],"status":"public","title":"Uniform approximation in classical weak convergence theory","year":"2019"},{"department":[{"_id":"355"}],"type":"conference_abstract","date_created":"2019-04-10T07:17:55Z","file":[{"date_created":"2019-04-10T07:17:17Z","creator":"wever","content_type":"application/pdf","success":1,"file_id":"8870","file_size":"74484","access_level":"closed","file_name":"Towards_Automated_Machine_Learning_for_Multi_Label_Classification.pdf","date_updated":"2019-04-10T07:17:17Z","relation":"main_file"}],"project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"citation":{"ieee":"M. D. Wever, F. Mohr, E. Hüllermeier, and A. Hetzer, “Towards Automated Machine Learning for Multi-Label Classification,” presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany, 2019.","mla":"Wever, Marcel Dominik, et al. <i>Towards Automated Machine Learning for Multi-Label Classification</i>. 2019.","apa":"Wever, M. D., Mohr, F., Hüllermeier, E., &#38; Hetzer, A. (2019). Towards Automated Machine Learning for Multi-Label Classification. Presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany.","bibtex":"@inproceedings{Wever_Mohr_Hüllermeier_Hetzer_2019, title={Towards Automated Machine Learning for Multi-Label Classification}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke and Hetzer, Alexander}, year={2019} }","chicago":"Wever, Marcel Dominik, Felix Mohr, Eyke Hüllermeier, and Alexander Hetzer. “Towards Automated Machine Learning for Multi-Label Classification,” 2019.","short":"M.D. Wever, F. Mohr, E. Hüllermeier, A. Hetzer, in: 2019.","ama":"Wever MD, Mohr F, Hüllermeier E, Hetzer A. Towards Automated Machine Learning for Multi-Label Classification. In: ; 2019."},"file_date_updated":"2019-04-10T07:17:17Z","ddc":["000"],"user_id":"49109","_id":"8868","language":[{"iso":"eng"}],"has_accepted_license":"1","date_updated":"2022-01-06T07:04:04Z","conference":{"location":"Bayreuth, Germany","name":"European Conference on Data Analytics (ECDA)","start_date":"2019-03-18","end_date":"2019-03-20"},"author":[{"id":"33176","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik"},{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke","id":"48129"},{"id":"38209","full_name":"Hetzer, Alexander","first_name":"Alexander","last_name":"Hetzer"}],"year":"2019","title":"Towards Automated Machine Learning for Multi-Label Classification","status":"public"},{"department":[{"_id":"34"},{"_id":"355"},{"_id":"7"}],"type":"journal_article","date_created":"2019-07-08T15:34:03Z","citation":{"mla":"Tagne, V. K., et al. “Choice Functions Generated by Mallows and Plackett–Luce Relations.” <i>New Mathematics and Natural Computation</i>, vol. 15, no. 2, 2019, pp. 191–213.","bibtex":"@article{Tagne_Fotso_Fono_Hüllermeier_2019, title={Choice Functions Generated by Mallows and Plackett–Luce Relations}, volume={15}, number={2}, journal={New Mathematics and Natural Computation}, author={Tagne, V. K. and Fotso, S. and Fono, L. A.  and Hüllermeier, Eyke}, year={2019}, pages={191–213} }","ama":"Tagne VK, Fotso S, Fono LA, Hüllermeier E. Choice Functions Generated by Mallows and Plackett–Luce Relations. <i>New Mathematics and Natural Computation</i>. 2019;15(2):191-213.","ieee":"V. K. Tagne, S. Fotso, L. A. Fono, and E. Hüllermeier, “Choice Functions Generated by Mallows and Plackett–Luce Relations,” <i>New Mathematics and Natural Computation</i>, vol. 15, no. 2, pp. 191–213, 2019.","apa":"Tagne, V. K., Fotso, S., Fono, L. A., &#38; Hüllermeier, E. (2019). Choice Functions Generated by Mallows and Plackett–Luce Relations. <i>New Mathematics and Natural Computation</i>, <i>15</i>(2), 191–213.","short":"V.K. Tagne, S. Fotso, L.A. Fono, E. Hüllermeier, New Mathematics and Natural Computation 15 (2019) 191–213.","chicago":"Tagne, V. K., S. Fotso, L. A.  Fono, and Eyke Hüllermeier. “Choice Functions Generated by Mallows and Plackett–Luce Relations.” <i>New Mathematics and Natural Computation</i> 15, no. 2 (2019): 191–213."},"issue":"2","publication":"New Mathematics and Natural Computation","volume":15,"user_id":"315","language":[{"iso":"eng"}],"_id":"10578","page":"191-213","intvolume":"        15","date_updated":"2022-01-06T06:50:45Z","author":[{"first_name":"V. K.","last_name":"Tagne","full_name":"Tagne, V. K."},{"full_name":"Fotso, S.","first_name":"S.","last_name":"Fotso"},{"last_name":"Fono","first_name":"L. A. ","full_name":"Fono, L. A. "},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"title":"Choice Functions Generated by Mallows and Plackett–Luce Relations","year":"2019","status":"public"},{"date_created":"2019-11-15T10:11:37Z","department":[{"_id":"34"},{"_id":"355"}],"type":"journal_article","citation":{"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. Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. <i>IEEE Computational Intelligence Magazine</i>. 2019:31-44. doi:<a href=\"https://doi.org/10.1109/mci.2018.2881642\">10.1109/mci.2018.2881642</a>","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>.","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>.","short":"I. Couso, C. Borgelt, E. Hüllermeier, R. Kruse, IEEE Computational Intelligence Magazine (2019) 31–44.","ieee":"I. Couso, C. Borgelt, E. Hüllermeier, and R. Kruse, “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning,” <i>IEEE Computational Intelligence Magazine</i>, pp. 31–44, 2019.","apa":"Couso, I., Borgelt, C., Hüllermeier, E., &#38; Kruse, R. (2019). Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. <i>IEEE Computational Intelligence Magazine</i>, 31–44. <a href=\"https://doi.org/10.1109/mci.2018.2881642\">https://doi.org/10.1109/mci.2018.2881642</a>"},"publication":"IEEE Computational Intelligence Magazine","_id":"15001","language":[{"iso":"eng"}],"page":"31-44","doi":"10.1109/mci.2018.2881642","user_id":"315","author":[{"first_name":"Ines","last_name":"Couso","full_name":"Couso, Ines"},{"full_name":"Borgelt, Christian","last_name":"Borgelt","first_name":"Christian"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"},{"first_name":"Rudolf","last_name":"Kruse","full_name":"Kruse, Rudolf"}],"publication_identifier":{"issn":["1556-603X","1556-6048"]},"year":"2019","title":"Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning","status":"public","date_updated":"2022-01-06T06:52:13Z","publication_status":"published"},{"publication":"Data Mining and Knowledge Discovery","issue":"2","abstract":[{"lang":"eng","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."}],"file":[{"date_created":"2020-02-28T12:43:39Z","creator":"lettmann","content_type":"application/pdf","file_id":"16155","file_size":837808,"access_level":"open_access","file_name":"multi-target-prediction.pdf","date_updated":"2020-02-28T12:45:26Z","relation":"main_file"}],"date_created":"2019-11-15T10:16:34Z","type":"journal_article","department":[{"_id":"34"},{"_id":"355"}],"title":"Multi-target prediction: a unifying view on problems and methods","year":"2019","author":[{"last_name":"Waegeman","first_name":"Willem","full_name":"Waegeman, Willem"},{"last_name":"Dembczynski","first_name":"Krzysztof","full_name":"Dembczynski, Krzysztof"},{"full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke","id":"48129"}],"publication_identifier":{"issn":["1573-756X"]},"date_updated":"2022-01-06T06:52:14Z","intvolume":"        33","language":[{"iso":"eng"}],"doi":"10.1007/s10618-018-0595-5","file_date_updated":"2020-02-28T12:45:26Z","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>.","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} }","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>","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.","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>","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. Hüllermeier, Data Mining and Knowledge Discovery 33 (2019) 293–324."},"oa":"1","status":"public","has_accepted_license":"1","page":"293-324","_id":"15002","ddc":["000"],"user_id":"315","volume":33},{"date_created":"2019-11-15T10:20:55Z","department":[{"_id":"34"},{"_id":"355"}],"type":"conference","citation":{"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.","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} }","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.","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.","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>.","short":"T. 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.","chicago":"Mortier, Thomas, Marek Wydmuch, Krzysztof Dembczynski, Eyke Hüllermeier, and Willem 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."},"publication":"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","language":[{"iso":"eng"}],"_id":"15003","user_id":"315","author":[{"full_name":"Mortier, Thomas","first_name":"Thomas","last_name":"Mortier"},{"full_name":"Wydmuch, Marek","first_name":"Marek","last_name":"Wydmuch"},{"last_name":"Dembczynski","first_name":"Krzysztof","full_name":"Dembczynski, Krzysztof"},{"full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke","id":"48129"},{"full_name":"Waegeman, Willem","last_name":"Waegeman","first_name":"Willem"}],"year":"2019","title":"Set-Valued Prediction in Multi-Class Classification","status":"public","date_updated":"2022-01-06T06:52:14Z"},{"citation":{"apa":"Ahmadi Fahandar, M., &#38; Hüllermeier, E. (2019). Feature Selection for Analogy-Based Learning to Rank. In <i>Discovery Science</i>. Cham. <a href=\"https://doi.org/10.1007/978-3-030-33778-0_22\">https://doi.org/10.1007/978-3-030-33778-0_22</a>","ieee":"M. Ahmadi Fahandar and E. Hüllermeier, “Feature Selection for Analogy-Based Learning to Rank,” in <i>Discovery Science</i>, Cham, 2019.","chicago":"Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Feature Selection for Analogy-Based Learning to Rank.” In <i>Discovery Science</i>. Cham, 2019. <a href=\"https://doi.org/10.1007/978-3-030-33778-0_22\">https://doi.org/10.1007/978-3-030-33778-0_22</a>.","short":"M. Ahmadi Fahandar, E. Hüllermeier, in: Discovery Science, Cham, 2019.","mla":"Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Feature Selection for Analogy-Based Learning to Rank.” <i>Discovery Science</i>, 2019, doi:<a href=\"https://doi.org/10.1007/978-3-030-33778-0_22\">10.1007/978-3-030-33778-0_22</a>.","ama":"Ahmadi Fahandar M, Hüllermeier E. Feature Selection for Analogy-Based Learning to Rank. In: <i>Discovery Science</i>. Cham; 2019. doi:<a href=\"https://doi.org/10.1007/978-3-030-33778-0_22\">10.1007/978-3-030-33778-0_22</a>","bibtex":"@inbook{Ahmadi Fahandar_Hüllermeier_2019, place={Cham}, title={Feature Selection for Analogy-Based Learning to Rank}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-33778-0_22\">10.1007/978-3-030-33778-0_22</a>}, booktitle={Discovery Science}, author={Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}, year={2019} }"},"publication":"Discovery Science","date_created":"2019-11-15T10:24:45Z","place":"Cham","department":[{"_id":"34"},{"_id":"355"}],"type":"book_chapter","publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783030337773","9783030337780"]},"author":[{"id":"59547","first_name":"Mohsen","last_name":"Ahmadi Fahandar","full_name":"Ahmadi Fahandar, Mohsen"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"year":"2019","title":"Feature Selection for Analogy-Based Learning to Rank","status":"public","publication_status":"published","date_updated":"2022-01-06T06:52:14Z","language":[{"iso":"eng"}],"_id":"15004","user_id":"315","doi":"10.1007/978-3-030-33778-0_22"},{"year":"2019","title":"Analogy-Based Preference Learning with Kernels","status":"public","author":[{"full_name":"Ahmadi Fahandar, Mohsen","last_name":"Ahmadi Fahandar","first_name":"Mohsen","id":"59547"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129"}],"publication_identifier":{"isbn":["9783030301781","9783030301798"],"issn":["0302-9743","1611-3349"]},"date_updated":"2022-01-06T06:52:14Z","publication_status":"published","language":[{"iso":"eng"}],"_id":"15005","doi":"10.1007/978-3-030-30179-8_3","user_id":"315","publication":"KI 2019: Advances in Artificial Intelligence","citation":{"ama":"Ahmadi Fahandar M, Hüllermeier E. Analogy-Based Preference Learning with Kernels. In: <i>KI 2019: Advances in Artificial Intelligence</i>. Cham; 2019. doi:<a href=\"https://doi.org/10.1007/978-3-030-30179-8_3\">10.1007/978-3-030-30179-8_3</a>","bibtex":"@inbook{Ahmadi Fahandar_Hüllermeier_2019, place={Cham}, title={Analogy-Based Preference Learning with Kernels}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-30179-8_3\">10.1007/978-3-030-30179-8_3</a>}, booktitle={KI 2019: Advances in Artificial Intelligence}, author={Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}, year={2019} }","mla":"Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Analogy-Based Preference Learning with Kernels.” <i>KI 2019: Advances in Artificial Intelligence</i>, 2019, doi:<a href=\"https://doi.org/10.1007/978-3-030-30179-8_3\">10.1007/978-3-030-30179-8_3</a>.","short":"M. Ahmadi Fahandar, E. Hüllermeier, in: KI 2019: Advances in Artificial Intelligence, Cham, 2019.","chicago":"Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Analogy-Based Preference Learning with Kernels.” In <i>KI 2019: Advances in Artificial Intelligence</i>. Cham, 2019. <a href=\"https://doi.org/10.1007/978-3-030-30179-8_3\">https://doi.org/10.1007/978-3-030-30179-8_3</a>.","apa":"Ahmadi Fahandar, M., &#38; Hüllermeier, E. (2019). Analogy-Based Preference Learning with Kernels. In <i>KI 2019: Advances in Artificial Intelligence</i>. Cham. <a href=\"https://doi.org/10.1007/978-3-030-30179-8_3\">https://doi.org/10.1007/978-3-030-30179-8_3</a>","ieee":"M. Ahmadi Fahandar and E. 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Adaptive confidence sets for kink estimation. <i>Electronic Journal of Statistics</i>, 1523–1579. <a href=\"https://doi.org/10.1214/19-ejs1555\">https://doi.org/10.1214/19-ejs1555</a>"},"date_created":"2019-10-30T14:25:16Z","type":"journal_article","department":[{"_id":"34"},{"_id":"355"}]},{"_id":"13132","series_title":"INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik","publisher":"Gesellschaft für Informatik e.V.","language":[{"iso":"eng"}],"page":" 273-274 ","user_id":"38209","conference":{"end_date":"2019-09-26","location":"Kassel","start_date":"2019-09-23","name":"Informatik 2019"},"author":[{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","id":"33176"},{"id":"38209","last_name":"Tornede","first_name":"Alexander","full_name":"Tornede, Alexander"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129"}],"year":"2019","status":"public","title":"From Automated to On-The-Fly Machine Learning","date_updated":"2022-01-06T06:51:28Z","place":"Bonn","date_created":"2019-09-04T08:44:46Z","department":[{"_id":"355"}],"type":"conference_abstract","citation":{"mla":"Mohr, Felix, et al. “From Automated to On-The-Fly Machine Learning.” <i>INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft</i>, Gesellschaft für Informatik e.V., 2019, pp. 273–74.","bibtex":"@inproceedings{Mohr_Wever_Tornede_Hüllermeier_2019, place={Bonn}, series={INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik}, title={From Automated to On-The-Fly Machine Learning}, booktitle={INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft}, publisher={Gesellschaft für Informatik e.V.}, author={Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier, Eyke}, year={2019}, pages={273–274}, collection={INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik} }","ama":"Mohr F, Wever MD, Tornede A, Hüllermeier E. From Automated to On-The-Fly Machine Learning. In: <i>INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft</i>. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik. Bonn: Gesellschaft für Informatik e.V.; 2019:273-274.","ieee":"F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “From Automated to On-The-Fly Machine Learning,” in <i>INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft</i>, Kassel, 2019, pp. 273–274.","apa":"Mohr, F., Wever, M. D., Tornede, A., &#38; Hüllermeier, E. (2019). From Automated to On-The-Fly Machine Learning. In <i>INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft</i> (pp. 273–274). Bonn: Gesellschaft für Informatik e.V.","chicago":"Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier. “From Automated to On-The-Fly Machine Learning.” In <i>INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft</i>, 273–74. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft Für Informatik. Bonn: Gesellschaft für Informatik e.V., 2019.","short":"F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, in: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, Gesellschaft für Informatik e.V., Bonn, 2019, pp. 273–274."},"publication":"INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"}]},{"status":"public","title":"Automating Multi-Label Classification Extending ML-Plan","year":"2019","author":[{"id":"33176","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik"},{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"id":"38209","full_name":"Tornede, Alexander","last_name":"Tornede","first_name":"Alexander"},{"id":"48129","last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"conference":{"start_date":"2019-06-09","name":"6th ICML Workshop on Automated Machine Learning (AutoML 2019)","location":"Long Beach, CA, USA","end_date":"2019-06-15"},"date_updated":"2022-01-06T06:50:33Z","has_accepted_license":"1","_id":"10232","language":[{"iso":"eng"}],"user_id":"33176","ddc":["006"],"file_date_updated":"2019-09-10T08:20:44Z","citation":{"apa":"Wever, M. D., Mohr, F., Tornede, A., &#38; Hüllermeier, E. (2019). Automating Multi-Label Classification Extending ML-Plan. Presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA.","mla":"Wever, Marcel Dominik, et al. <i>Automating Multi-Label Classification Extending ML-Plan</i>. 2019.","ieee":"M. D. Wever, F. Mohr, A. Tornede, and E. Hüllermeier, “Automating Multi-Label Classification Extending ML-Plan,” presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA, 2019.","short":"M.D. Wever, F. Mohr, A. Tornede, E. Hüllermeier, in: 2019.","ama":"Wever MD, Mohr F, Tornede A, Hüllermeier E. Automating Multi-Label Classification Extending ML-Plan. In: ; 2019.","chicago":"Wever, Marcel Dominik, Felix Mohr, Alexander Tornede, and Eyke Hüllermeier. “Automating Multi-Label Classification Extending ML-Plan,” 2019.","bibtex":"@inproceedings{Wever_Mohr_Tornede_Hüllermeier_2019, title={Automating Multi-Label Classification Extending ML-Plan}, author={Wever, Marcel Dominik and Mohr, Felix and Tornede, Alexander and Hüllermeier, Eyke}, year={2019} }"},"abstract":[{"text":"Existing tools for automated machine learning, such as Auto-WEKA, TPOT, auto-sklearn, and more recently ML-Plan, have shown impressive results for the tasks of single-label classification and regression. Yet, there is only little work on other types of machine learning problems so far. In particular, there is almost no work on automating the engineering of machine learning solutions for multi-label classification (MLC). We show how the scope of ML-Plan, an AutoML-tool for multi-class classification, can be extended towards MLC using MEKA, which is a multi-label extension of the well-known Java library WEKA. The resulting approach recursively refines MEKA's multi-label classifiers, nesting other multi-label classifiers for meta algorithms and single-label classifiers provided by WEKA as base learners. In our evaluation, we find that the proposed approach yields strong results and performs significantly better than a set of baselines we compare with.","lang":"eng"}],"project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"file":[{"date_created":"2019-09-10T08:19:01Z","creator":"wever","file_id":"13177","content_type":"application/pdf","file_name":"Automating_MultiLabel_Classification_Extending_ML-Plan.pdf","access_level":"open_access","file_size":388191,"relation":"main_file","date_updated":"2019-09-10T08:20:44Z"}],"date_created":"2019-06-11T21:33:06Z","type":"conference","department":[{"_id":"355"}],"oa":"1"},{"date_updated":"2023-02-01T12:39:19Z","author":[{"full_name":"Rohlfing, Katharina","last_name":"Rohlfing","first_name":"Katharina","id":"50352"},{"first_name":"Giuseppe","last_name":"Leonardi","full_name":"Leonardi, Giuseppe"},{"last_name":"Nomikou","first_name":"Iris","full_name":"Nomikou, Iris"},{"first_name":"Joanna","last_name":"Rączaszek-Leonardi","full_name":"Rączaszek-Leonardi, Joanna"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"}],"status":"public","year":"2019","title":"Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches","doi":"10.1109/TCDS.2019.2892991","user_id":"14931","_id":"20243","language":[{"iso":"eng"}],"citation":{"ama":"Rohlfing K, Leonardi G, Nomikou I, Rączaszek-Leonardi J, Hüllermeier E. Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. <i>IEEE Transactions on Cognitive and Developmental Systems</i>. Published online 2019. doi:<a href=\"https://doi.org/10.1109/TCDS.2019.2892991\">10.1109/TCDS.2019.2892991</a>","bibtex":"@article{Rohlfing_Leonardi_Nomikou_Rączaszek-Leonardi_Hüllermeier_2019, title={Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches}, DOI={<a href=\"https://doi.org/10.1109/TCDS.2019.2892991\">10.1109/TCDS.2019.2892991</a>}, journal={IEEE Transactions on Cognitive and Developmental Systems}, author={Rohlfing, Katharina and Leonardi, Giuseppe and Nomikou, Iris and Rączaszek-Leonardi, Joanna and Hüllermeier, Eyke}, year={2019} }","mla":"Rohlfing, Katharina, et al. “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches.” <i>IEEE Transactions on Cognitive and Developmental Systems</i>, 2019, doi:<a href=\"https://doi.org/10.1109/TCDS.2019.2892991\">10.1109/TCDS.2019.2892991</a>.","chicago":"Rohlfing, Katharina, Giuseppe Leonardi, Iris Nomikou, Joanna Rączaszek-Leonardi, and Eyke Hüllermeier. “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches.” <i>IEEE Transactions on Cognitive and Developmental Systems</i>, 2019. <a href=\"https://doi.org/10.1109/TCDS.2019.2892991\">https://doi.org/10.1109/TCDS.2019.2892991</a>.","short":"K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, E. Hüllermeier, IEEE Transactions on Cognitive and Developmental Systems (2019).","apa":"Rohlfing, K., Leonardi, G., Nomikou, I., Rączaszek-Leonardi, J., &#38; Hüllermeier, E. (2019). Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. <i>IEEE Transactions on Cognitive and Developmental Systems</i>. <a href=\"https://doi.org/10.1109/TCDS.2019.2892991\">https://doi.org/10.1109/TCDS.2019.2892991</a>","ieee":"K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, and E. Hüllermeier, “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches,” <i>IEEE Transactions on Cognitive and Developmental Systems</i>, 2019, doi: <a href=\"https://doi.org/10.1109/TCDS.2019.2892991\">10.1109/TCDS.2019.2892991</a>."},"publication":"IEEE Transactions on Cognitive and Developmental Systems","department":[{"_id":"749"},{"_id":"355"}],"type":"journal_article","date_created":"2020-11-02T13:25:49Z"}]