[{"user_id":"5786","language":[{"iso":"eng"}],"_id":"20306","date_updated":"2022-01-06T06:54:26Z","status":"public","title":"Towards Meta-Algorithm Selection","year":"2020","conference":{"location":"Online","name":"Workshop MetaLearn 2020 @ NeurIPS 2020"},"author":[{"id":"38209","last_name":"Tornede","first_name":"Alexander","full_name":"Tornede, Alexander"},{"last_name":"Wever","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","id":"33176"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129"}],"type":"conference","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"date_created":"2020-11-06T09:42:27Z","project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"publication":"Workshop MetaLearn 2020 @ NeurIPS 2020","citation":{"mla":"Tornede, Alexander, et al. “Towards Meta-Algorithm Selection.” <i>Workshop MetaLearn 2020 @ NeurIPS 2020</i>, 2020.","bibtex":"@inproceedings{Tornede_Wever_Hüllermeier_2020, title={Towards Meta-Algorithm Selection}, booktitle={Workshop MetaLearn 2020 @ NeurIPS 2020}, author={Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020} }","ama":"Tornede A, Wever MD, Hüllermeier E. Towards Meta-Algorithm Selection. In: <i>Workshop MetaLearn 2020 @ NeurIPS 2020</i>. ; 2020.","ieee":"A. Tornede, M. D. Wever, and E. Hüllermeier, “Towards Meta-Algorithm Selection,” presented at the Workshop MetaLearn 2020 @ NeurIPS 2020, Online, 2020.","apa":"Tornede, A., Wever, M. D., &#38; Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. <i>Workshop MetaLearn 2020 @ NeurIPS 2020</i>. Workshop MetaLearn 2020 @ NeurIPS 2020, Online.","chicago":"Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Towards Meta-Algorithm Selection.” In <i>Workshop MetaLearn 2020 @ NeurIPS 2020</i>, 2020.","short":"A. Tornede, M.D. Wever, E. Hüllermeier, in: Workshop MetaLearn 2020 @ NeurIPS 2020, 2020."}},{"type":"book_chapter","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"date_created":"2020-08-17T11:44:37Z","publication":"Learning and Intelligent Optimization. LION 2020.","doi":"10.1007/978-3-030-53552-0_22","series_title":"Lecture Notes in Computer Science","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:53:25Z","publication_status":"published","intvolume":"     12096","title":"Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach","year":"2020","publication_identifier":{"isbn":["9783030535513","9783030535520"],"issn":["0302-9743","1611-3349"]},"author":[{"first_name":"Adil","last_name":"El Mesaoudi-Paul","full_name":"El Mesaoudi-Paul, Adil"},{"first_name":"Dimitri","last_name":"Weiß","full_name":"Weiß, Dimitri"},{"id":"76599","full_name":"Bengs, Viktor","first_name":"Viktor","last_name":"Bengs"},{"id":"48129","first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"},{"full_name":"Tierney, Kevin","last_name":"Tierney","first_name":"Kevin"}],"place":"Cham","project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"citation":{"mla":"El Mesaoudi-Paul, Adil, et al. “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach.” <i>Learning and Intelligent Optimization. LION 2020.</i>, vol. 12096, Springer, 2020, pp. 216–32, doi:<a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">10.1007/978-3-030-53552-0_22</a>.","bibtex":"@inbook{El Mesaoudi-Paul_Weiß_Bengs_Hüllermeier_Tierney_2020, place={Cham}, series={Lecture Notes in Computer Science}, title={Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach}, volume={12096}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">10.1007/978-3-030-53552-0_22</a>}, booktitle={Learning and Intelligent Optimization. LION 2020.}, publisher={Springer}, author={El Mesaoudi-Paul, Adil and Weiß, Dimitri and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin}, year={2020}, pages={216–232}, collection={Lecture Notes in Computer Science} }","ama":"El Mesaoudi-Paul A, Weiß D, Bengs V, Hüllermeier E, Tierney K. Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In: <i>Learning and Intelligent Optimization. LION 2020.</i> Vol 12096. Lecture Notes in Computer Science. Cham: Springer; 2020:216-232. doi:<a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">10.1007/978-3-030-53552-0_22</a>","ieee":"A. El Mesaoudi-Paul, D. Weiß, V. Bengs, E. Hüllermeier, and K. Tierney, “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach,” in <i>Learning and Intelligent Optimization. LION 2020.</i>, vol. 12096, Cham: Springer, 2020, pp. 216–232.","apa":"El Mesaoudi-Paul, A., Weiß, D., Bengs, V., Hüllermeier, E., &#38; Tierney, K. (2020). Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In <i>Learning and Intelligent Optimization. LION 2020.</i> (Vol. 12096, pp. 216–232). Cham: Springer. <a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">https://doi.org/10.1007/978-3-030-53552-0_22</a>","chicago":"El Mesaoudi-Paul, Adil, Dimitri Weiß, Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach.” In <i>Learning and Intelligent Optimization. LION 2020.</i>, 12096:216–32. Lecture Notes in Computer Science. Cham: Springer, 2020. <a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">https://doi.org/10.1007/978-3-030-53552-0_22</a>.","short":"A. El Mesaoudi-Paul, D. Weiß, V. Bengs, E. Hüllermeier, K. Tierney, in: Learning and Intelligent Optimization. LION 2020., Springer, Cham, 2020, pp. 216–232."},"user_id":"76599","volume":12096,"page":"216 - 232","_id":"18014","publisher":"Springer","status":"public"},{"_id":"18017","language":[{"iso":"eng"}],"user_id":"76599","status":"public","title":"Online Preselection with Context Information under the Plackett-Luce  Model","year":"2020","author":[{"last_name":"El Mesaoudi-Paul","first_name":"Adil","full_name":"El Mesaoudi-Paul, Adil"},{"full_name":"Bengs, Viktor","first_name":"Viktor","last_name":"Bengs","id":"76599"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"}],"publication_status":"draft","date_updated":"2022-01-06T06:53:25Z","date_created":"2020-08-17T11:49:40Z","type":"preprint","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"publication":"arXiv:2002.04275","citation":{"short":"A. El Mesaoudi-Paul, V. Bengs, E. Hüllermeier, ArXiv:2002.04275 (n.d.).","ama":"El Mesaoudi-Paul A, Bengs V, Hüllermeier E. Online Preselection with Context Information under the Plackett-Luce  Model. <i>arXiv:200204275</i>.","chicago":"El Mesaoudi-Paul, Adil, Viktor Bengs, and Eyke Hüllermeier. “Online Preselection with Context Information under the Plackett-Luce  Model.” <i>ArXiv:2002.04275</i>, n.d.","bibtex":"@article{El Mesaoudi-Paul_Bengs_Hüllermeier, title={Online Preselection with Context Information under the Plackett-Luce  Model}, journal={arXiv:2002.04275}, author={El Mesaoudi-Paul, Adil and Bengs, Viktor and Hüllermeier, Eyke} }","mla":"El Mesaoudi-Paul, Adil, et al. “Online Preselection with Context Information under the Plackett-Luce  Model.” <i>ArXiv:2002.04275</i>.","apa":"El Mesaoudi-Paul, A., Bengs, V., &#38; Hüllermeier, E. (n.d.). Online Preselection with Context Information under the Plackett-Luce  Model. <i>ArXiv:2002.04275</i>.","ieee":"A. El Mesaoudi-Paul, V. Bengs, and E. Hüllermeier, “Online Preselection with Context Information under the Plackett-Luce  Model,” <i>arXiv:2002.04275</i>. ."},"abstract":[{"lang":"eng","text":"We consider an extension of the contextual multi-armed bandit problem, in\r\nwhich, instead of selecting a single alternative (arm), a learner is supposed\r\nto make a preselection in the form of a subset of alternatives. More\r\nspecifically, in each iteration, the learner is presented a set of arms and a\r\ncontext, both described in terms of feature vectors. The task of the learner is\r\nto preselect $k$ of these arms, among which a final choice is made in a second\r\nstep. In our setup, we assume that each arm has a latent (context-dependent)\r\nutility, and that feedback on a preselection is produced according to a\r\nPlackett-Luce model. We propose the CPPL algorithm, which is inspired by the\r\nwell-known UCB algorithm, and evaluate this algorithm on synthetic and real\r\ndata. In particular, we consider an online algorithm selection scenario, which\r\nserved as a main motivation of our problem setting. Here, an instance (which\r\ndefines the context) from a certain problem class (such as SAT) can be solved\r\nby different algorithms (the arms), but only $k$ of these algorithms can\r\nactually be run."}],"project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}]},{"date_updated":"2022-01-06T06:53:28Z","conference":{"name":"12th Asian Conference on Machine Learning","start_date":"2020-11-18","location":"Bangkok, Thailand","end_date":"2020-11-20"},"author":[{"full_name":"Tornede, Alexander","first_name":"Alexander","last_name":"Tornede","id":"38209"},{"full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik","id":"33176"},{"full_name":"Werner, Stefan","first_name":"Stefan","last_name":"Werner"},{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129"}],"status":"public","title":"Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis","year":"2020","user_id":"5786","language":[{"iso":"eng"}],"_id":"18276","main_file_link":[{"url":"https://arxiv.org/pdf/2007.02816.pdf"}],"project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"abstract":[{"text":"Algorithm selection (AS) deals with the automatic selection of an algorithm\r\nfrom a fixed set of candidate algorithms most suitable for a specific instance\r\nof an algorithmic problem class, where \"suitability\" often refers to an\r\nalgorithm's runtime. Due to possibly extremely long runtimes of candidate\r\nalgorithms, training data for algorithm selection models is usually generated\r\nunder time constraints in the sense that not all algorithms are run to\r\ncompletion on all instances. Thus, training data usually comprises censored\r\ninformation, as the true runtime of algorithms timed out remains unknown.\r\nHowever, many standard AS approaches are not able to handle such information in\r\na proper way. On the other side, survival analysis (SA) naturally supports\r\ncensored data and offers appropriate ways to use such data for learning\r\ndistributional models of algorithm runtime, as we demonstrate in this work. We\r\nleverage such models as a basis of a sophisticated decision-theoretic approach\r\nto algorithm selection, which we dub Run2Survive. Moreover, taking advantage of\r\na framework of this kind, we advocate a risk-averse approach to algorithm\r\nselection, in which the avoidance of a timeout is given high priority. In an\r\nextensive experimental study with the standard benchmark ASlib, our approach is\r\nshown to be highly competitive and in many cases even superior to\r\nstate-of-the-art AS approaches.","lang":"eng"}],"citation":{"apa":"Tornede, A., Wever, M. D., Werner, S., Mohr, F., &#38; Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. <i>ACML 2020</i>. 12th Asian Conference on Machine Learning, Bangkok, Thailand.","ieee":"A. Tornede, M. D. Wever, S. Werner, F. Mohr, and E. Hüllermeier, “Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis,” presented at the 12th Asian Conference on Machine Learning, Bangkok, Thailand, 2020.","chicago":"Tornede, Alexander, Marcel Dominik Wever, Stefan Werner, Felix Mohr, and Eyke Hüllermeier. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” In <i>ACML 2020</i>, 2020.","short":"A. Tornede, M.D. Wever, S. Werner, F. Mohr, E. Hüllermeier, in: ACML 2020, 2020.","mla":"Tornede, Alexander, et al. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” <i>ACML 2020</i>, 2020.","ama":"Tornede A, Wever MD, Werner S, Mohr F, Hüllermeier E. Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. In: <i>ACML 2020</i>. ; 2020.","bibtex":"@inproceedings{Tornede_Wever_Werner_Mohr_Hüllermeier_2020, title={Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis}, booktitle={ACML 2020}, author={Tornede, Alexander and Wever, Marcel Dominik and Werner, Stefan and Mohr, Felix and Hüllermeier, Eyke}, year={2020} }"},"publication":"ACML 2020","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"type":"conference","date_created":"2020-08-25T12:09:28Z"},{"department":[{"_id":"7"},{"_id":"77"},{"_id":"355"}],"type":"journal_article","date_created":"2020-04-19T14:08:06Z","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B3","_id":"11"},{"_id":"12","name":"SFB 901 - Subproject B4"}],"citation":{"mla":"Richter, Cedric, et al. “Algorithm Selection for Software Validation Based on Graph Kernels.” <i>Journal of Automated Software Engineering</i>, Springer.","ama":"Richter C, Hüllermeier E, Jakobs M-C, Wehrheim H. Algorithm Selection for Software Validation Based on Graph Kernels. <i>Journal of Automated Software Engineering</i>.","bibtex":"@article{Richter_Hüllermeier_Jakobs_Wehrheim, title={Algorithm Selection for Software Validation Based on Graph Kernels}, journal={Journal of Automated Software Engineering}, publisher={Springer}, author={Richter, Cedric and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike} }","apa":"Richter, C., Hüllermeier, E., Jakobs, M.-C., &#38; Wehrheim, H. (n.d.). Algorithm Selection for Software Validation Based on Graph Kernels. <i>Journal of Automated Software Engineering</i>.","ieee":"C. Richter, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Algorithm Selection for Software Validation Based on Graph Kernels,” <i>Journal of Automated Software Engineering</i>.","short":"C. Richter, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, Journal of Automated Software Engineering (n.d.).","chicago":"Richter, Cedric, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim. “Algorithm Selection for Software Validation Based on Graph Kernels.” <i>Journal of Automated Software Engineering</i>, n.d."},"publication":"Journal of Automated Software Engineering","user_id":"477","_id":"16725","publisher":"Springer","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:52:55Z","publication_status":"accepted","author":[{"full_name":"Richter, Cedric","first_name":"Cedric","last_name":"Richter","id":"50003"},{"full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke","id":"48129"},{"full_name":"Jakobs, Marie-Christine","last_name":"Jakobs","first_name":"Marie-Christine"},{"id":"573","full_name":"Wehrheim, Heike","last_name":"Wehrheim","first_name":"Heike"}],"title":"Algorithm Selection for Software Validation Based on Graph Kernels","status":"public","year":"2020"},{"user_id":"5786","publisher":"Springer","_id":"15629","language":[{"iso":"eng"}],"publication_status":"accepted","date_updated":"2022-01-06T06:52:30Z","year":"2020","title":"LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification","status":"public","author":[{"full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","last_name":"Wever","id":"33176"},{"id":"38209","full_name":"Tornede, Alexander","last_name":"Tornede","first_name":"Alexander"},{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"conference":{"location":"Konstanz, Germany","name":"Symposium on Intelligent Data Analysis","start_date":"2020-04-24","end_date":"2020-04-27"},"type":"conference","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"date_created":"2020-01-23T08:44:08Z","abstract":[{"text":"In multi-label classification (MLC), each instance is associated with a set of class labels, in contrast to standard classification where an instance is assigned a single label. Binary relevance (BR) learning, which reduces a multi-label to a set of binary classification problems, one per label, is arguably the most straight-forward approach to MLC. In spite of its simplicity, BR proved to be competitive to more sophisticated MLC methods, and still achieves state-of-the-art performance for many loss functions. Somewhat surprisingly, the optimal choice of the base learner for tackling the binary classification problems has received very little attention so far. Taking advantage of the label independence assumption inherent to BR, we propose a label-wise base learner selection method optimizing label-wise macro averaged performance measures. In an extensive experimental evaluation, we find that or approach, called LiBRe, can significantly improve generalization performance.","lang":"eng"}],"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"citation":{"bibtex":"@inproceedings{Wever_Tornede_Mohr_Hüllermeier, title={LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification}, publisher={Springer}, author={Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke} }","ama":"Wever MD, Tornede A, Mohr F, Hüllermeier E. LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. In: Springer.","mla":"Wever, Marcel Dominik, et al. <i>LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification</i>. Springer.","chicago":"Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification.” Springer, n.d.","short":"M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, in: Springer, n.d.","ieee":"M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification,” presented at the Symposium on Intelligent Data Analysis, Konstanz, Germany.","apa":"Wever, M. D., Tornede, A., Mohr, F., &#38; Hüllermeier, E. (n.d.). <i>LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification</i>. Symposium on Intelligent Data Analysis, Konstanz, Germany."}},{"_id":"8868","language":[{"iso":"eng"}],"ddc":["000"],"user_id":"49109","conference":{"start_date":"2019-03-18","name":"European Conference on Data Analytics (ECDA)","location":"Bayreuth, Germany","end_date":"2019-03-20"},"author":[{"orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","id":"33176"},{"last_name":"Mohr","first_name":"Felix","full_name":"Mohr, Felix"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, 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","has_accepted_license":"1","date_updated":"2022-01-06T07:04:04Z","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","access_level":"closed","file_size":"74484","file_name":"Towards_Automated_Machine_Learning_for_Multi_Label_Classification.pdf","date_updated":"2019-04-10T07:17:17Z","relation":"main_file"}],"department":[{"_id":"355"}],"type":"conference_abstract","citation":{"ama":"Wever MD, Mohr F, Hüllermeier E, Hetzer A. Towards Automated Machine Learning for Multi-Label Classification. In: ; 2019.","short":"M.D. Wever, F. Mohr, E. Hüllermeier, A. Hetzer, in: 2019.","chicago":"Wever, Marcel Dominik, Felix Mohr, Eyke Hüllermeier, and Alexander Hetzer. “Towards Automated Machine Learning for Multi-Label Classification,” 2019.","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} }","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.","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."},"file_date_updated":"2019-04-10T07:17:17Z","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}]},{"user_id":"315","volume":15,"page":"191-213","language":[{"iso":"eng"}],"_id":"10578","date_updated":"2022-01-06T06:50:45Z","intvolume":"        15","year":"2019","status":"public","title":"Choice Functions Generated by Mallows and Plackett–Luce Relations","author":[{"full_name":"Tagne, V. K.","first_name":"V. K.","last_name":"Tagne"},{"full_name":"Fotso, S.","last_name":"Fotso","first_name":"S."},{"first_name":"L. A. ","last_name":"Fono","full_name":"Fono, L. A. "},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier","id":"48129"}],"type":"journal_article","department":[{"_id":"34"},{"_id":"355"},{"_id":"7"}],"date_created":"2019-07-08T15:34:03Z","issue":"2","publication":"New Mathematics and Natural Computation","citation":{"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.","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.","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.","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."}},{"date_created":"2019-11-15T10:11:37Z","department":[{"_id":"34"},{"_id":"355"}],"type":"journal_article","citation":{"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>","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.","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.","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>.","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>","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} }"},"publication":"IEEE Computational Intelligence Magazine","language":[{"iso":"eng"}],"_id":"15001","page":"31-44","doi":"10.1109/mci.2018.2881642","user_id":"315","author":[{"last_name":"Couso","first_name":"Ines","full_name":"Couso, Ines"},{"full_name":"Borgelt, Christian","first_name":"Christian","last_name":"Borgelt"},{"id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier"},{"full_name":"Kruse, Rudolf","last_name":"Kruse","first_name":"Rudolf"}],"publication_identifier":{"issn":["1556-603X","1556-6048"]},"status":"public","year":"2019","title":"Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning","date_updated":"2022-01-06T06:52:13Z","publication_status":"published"},{"oa":"1","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>.","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. Hüllermeier, Data Mining and Knowledge Discovery 33 (2019) 293–324."},"page":"293-324","_id":"15002","ddc":["000"],"user_id":"315","volume":33,"status":"public","has_accepted_license":"1","file":[{"creator":"lettmann","date_created":"2020-02-28T12:43:39Z","file_name":"multi-target-prediction.pdf","access_level":"open_access","file_size":837808,"relation":"main_file","date_updated":"2020-02-28T12:45:26Z","file_id":"16155","content_type":"application/pdf"}],"date_created":"2019-11-15T10:16:34Z","type":"journal_article","department":[{"_id":"34"},{"_id":"355"}],"issue":"2","publication":"Data Mining and Knowledge Discovery","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."}],"language":[{"iso":"eng"}],"doi":"10.1007/s10618-018-0595-5","year":"2019","title":"Multi-target prediction: a unifying view on problems and methods","publication_identifier":{"issn":["1573-756X"]},"author":[{"full_name":"Waegeman, Willem","first_name":"Willem","last_name":"Waegeman"},{"last_name":"Dembczynski","first_name":"Krzysztof","full_name":"Dembczynski, Krzysztof"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"}],"date_updated":"2022-01-06T06:52:14Z","intvolume":"        33"},{"date_created":"2019-11-15T10:20:55Z","type":"conference","department":[{"_id":"34"},{"_id":"355"}],"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","citation":{"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.","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.","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.","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>."},"_id":"15003","language":[{"iso":"eng"}],"user_id":"315","year":"2019","title":"Set-Valued Prediction in Multi-Class Classification","status":"public","author":[{"full_name":"Mortier, Thomas","last_name":"Mortier","first_name":"Thomas"},{"first_name":"Marek","last_name":"Wydmuch","full_name":"Wydmuch, Marek"},{"full_name":"Dembczynski, Krzysztof","last_name":"Dembczynski","first_name":"Krzysztof"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"},{"full_name":"Waegeman, Willem","first_name":"Willem","last_name":"Waegeman"}],"date_updated":"2022-01-06T06:52:14Z"},{"publication_identifier":{"isbn":["9783030337773","9783030337780"],"issn":["0302-9743","1611-3349"]},"author":[{"id":"59547","full_name":"Ahmadi Fahandar, Mohsen","last_name":"Ahmadi Fahandar","first_name":"Mohsen"},{"id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier"}],"title":"Feature Selection for Analogy-Based Learning to Rank","year":"2019","status":"public","publication_status":"published","date_updated":"2022-01-06T06:52:14Z","_id":"15004","language":[{"iso":"eng"}],"user_id":"315","doi":"10.1007/978-3-030-33778-0_22","citation":{"short":"M. Ahmadi Fahandar, E. Hüllermeier, in: Discovery Science, 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>.","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.","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} }","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>."},"publication":"Discovery Science","date_created":"2019-11-15T10:24:45Z","place":"Cham","department":[{"_id":"34"},{"_id":"355"}],"type":"book_chapter"},{"language":[{"iso":"eng"}],"_id":"15005","user_id":"315","doi":"10.1007/978-3-030-30179-8_3","status":"public","title":"Analogy-Based Preference Learning with Kernels","year":"2019","publication_identifier":{"isbn":["9783030301781","9783030301798"],"issn":["0302-9743","1611-3349"]},"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_status":"published","date_updated":"2022-01-06T06:52:14Z","date_created":"2019-11-15T10:30:10Z","place":"Cham","type":"book_chapter","department":[{"_id":"34"},{"_id":"355"}],"publication":"KI 2019: Advances in Artificial Intelligence","citation":{"apa":"Ahmadi Fahandar, M., &#38; Hüllermeier, E. 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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>.","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>.","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} }"}},{"publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783030337773","9783030337780"]},"author":[{"last_name":"Nguyen","first_name":"Vu-Linh","full_name":"Nguyen, Vu-Linh"},{"full_name":"Destercke, Sébastien","first_name":"Sébastien","last_name":"Destercke"},{"id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier"}],"status":"public","title":"Epistemic Uncertainty Sampling","year":"2019","date_updated":"2022-01-06T06:52:14Z","publication_status":"published","language":[{"iso":"eng"}],"_id":"15006","doi":"10.1007/978-3-030-33778-0_7","user_id":"49109","citation":{"chicago":"Nguyen, Vu-Linh, Sébastien Destercke, and Eyke Hüllermeier. “Epistemic Uncertainty Sampling.” In <i>Discovery Science</i>. Cham, 2019. <a href=\"https://doi.org/10.1007/978-3-030-33778-0_7\">https://doi.org/10.1007/978-3-030-33778-0_7</a>.","short":"V.-L. Nguyen, S. Destercke, E. Hüllermeier, in: Discovery Science, Cham, 2019.","apa":"Nguyen, V.-L., Destercke, S., &#38; Hüllermeier, E. (2019). Epistemic Uncertainty Sampling. In <i>Discovery Science</i>. Cham. <a href=\"https://doi.org/10.1007/978-3-030-33778-0_7\">https://doi.org/10.1007/978-3-030-33778-0_7</a>","ieee":"V.-L. Nguyen, S. Destercke, and E. Hüllermeier, “Epistemic Uncertainty Sampling,” in <i>Discovery Science</i>, Cham, 2019.","ama":"Nguyen V-L, Destercke S, Hüllermeier E. Epistemic Uncertainty Sampling. In: <i>Discovery Science</i>. Cham; 2019. doi:<a href=\"https://doi.org/10.1007/978-3-030-33778-0_7\">10.1007/978-3-030-33778-0_7</a>","bibtex":"@inbook{Nguyen_Destercke_Hüllermeier_2019, place={Cham}, title={Epistemic Uncertainty Sampling}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-33778-0_7\">10.1007/978-3-030-33778-0_7</a>}, booktitle={Discovery Science}, author={Nguyen, Vu-Linh and Destercke, Sébastien and Hüllermeier, Eyke}, year={2019} }","mla":"Nguyen, Vu-Linh, et al. “Epistemic Uncertainty Sampling.” <i>Discovery Science</i>, 2019, doi:<a href=\"https://doi.org/10.1007/978-3-030-33778-0_7\">10.1007/978-3-030-33778-0_7</a>."},"publication":"Discovery Science","place":"Cham","date_created":"2019-11-15T10:35:08Z","department":[{"_id":"34"},{"_id":"355"}],"type":"book_chapter"},{"department":[{"_id":"34"},{"_id":"355"},{"_id":"7"}],"type":"conference","date_created":"2019-11-15T10:43:26Z","file":[{"date_updated":"2020-02-28T12:47:07Z","relation":"main_file","file_size":2331320,"access_level":"open_access","file_name":"learning-to-aggregate-owa.pdf","content_type":"application/pdf","file_id":"16156","creator":"lettmann","date_created":"2020-02-28T12:47:07Z"}],"publication":"Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101)","doi":"10.1016/j.jmva.2019.02.017","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:52:14Z","publication_status":"published","author":[{"id":"58747","last_name":"Melnikov","first_name":"Vitaly","full_name":"Melnikov, Vitaly"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier","id":"48129"}],"year":"2019","title":"Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA","oa":"1","project":[{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901","_id":"1"}],"citation":{"short":"V. Melnikov, E. Hüllermeier, in: Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019.","chicago":"Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA.” In <i>Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101)</i>, 2019. <a href=\"https://doi.org/10.1016/j.jmva.2019.02.017\">https://doi.org/10.1016/j.jmva.2019.02.017</a>.","ieee":"V. Melnikov and E. Hüllermeier, “Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA,” in <i>Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101)</i>, 2019.","apa":"Melnikov, V., &#38; Hüllermeier, E. (2019). Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA. In <i>Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101)</i>. <a href=\"https://doi.org/10.1016/j.jmva.2019.02.017\">https://doi.org/10.1016/j.jmva.2019.02.017</a>","bibtex":"@inproceedings{Melnikov_Hüllermeier_2019, title={Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA}, DOI={<a href=\"https://doi.org/10.1016/j.jmva.2019.02.017\">10.1016/j.jmva.2019.02.017</a>}, booktitle={Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101)}, author={Melnikov, Vitaly and Hüllermeier, Eyke}, year={2019} }","ama":"Melnikov V, Hüllermeier E. Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA. 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