[{"author":[{"id":"39640","last_name":"Heid","full_name":"Heid, Stefan Helmut","orcid":"0000-0002-9461-7372","first_name":"Stefan Helmut"},{"first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","id":"33176"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"}],"publisher":"episciences","publication":"Journal of Data Mining and Digital Humanities","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"status":"public","project":[{"_id":"39","name":"InterGramm"}],"date_created":"2020-08-05T06:52:53Z","publication_status":"submitted","abstract":[{"lang":"eng","text":"Syntactic annotation of corpora in the form of part-of-speech (POS) tags is a key requirement for both linguistic research and subsequent automated natural language processing (NLP) tasks. This problem is commonly tackled using machine learning methods, i.e., by training a POS tagger on a sufficiently large corpus of labeled data. \r\nWhile the problem of POS tagging can essentially be considered as solved for modern languages, historical corpora turn out to be much more difficult, especially due to the lack of native speakers and sparsity of training data. Moreover, most texts have no sentences as we know them today, nor a common orthography.\r\nThese irregularities render the task of automated POS tagging more difficult and error-prone. Under these circumstances, instead of forcing the POS tagger to predict and commit to a single tag, it should be enabled to express its uncertainty. In this paper, we consider POS tagging within the framework of set-valued prediction, which allows the POS tagger to express its uncertainty via predicting a set of candidate POS tags instead of guessing a single one. The goal is to guarantee a high confidence that the correct POS tag is included while keeping the number of candidates small.\r\nIn our experimental study, we find that extending state-of-the-art POS taggers to set-valued prediction yields more precise and robust taggings, especially for unknown words, i.e., words not occurring in the training data."}],"user_id":"5786","title":"Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction","main_file_link":[{"url":"https://arxiv.org/abs/2008.01377","open_access":"1"}],"language":[{"iso":"eng"}],"year":"2020","type":"preprint","citation":{"bibtex":"@article{Heid_Wever_Hüllermeier, title={Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction}, journal={Journal of Data Mining and Digital Humanities}, publisher={episciences}, author={Heid, Stefan Helmut and Wever, Marcel Dominik and Hüllermeier, Eyke} }","mla":"Heid, Stefan Helmut, et al. “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction.” Journal of Data Mining and Digital Humanities, episciences.","chicago":"Heid, Stefan Helmut, Marcel Dominik Wever, and Eyke Hüllermeier. “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction.” Journal of Data Mining and Digital Humanities. episciences, n.d.","ama":"Heid SH, Wever MD, Hüllermeier E. Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. Journal of Data Mining and Digital Humanities.","apa":"Heid, S. H., Wever, M. D., & Hüllermeier, E. (n.d.). Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. In Journal of Data Mining and Digital Humanities. episciences.","ieee":"S. H. Heid, M. D. Wever, and E. Hüllermeier, “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction,” Journal of Data Mining and Digital Humanities. episciences.","short":"S.H. Heid, M.D. Wever, E. Hüllermeier, Journal of Data Mining and Digital Humanities (n.d.)."},"date_updated":"2022-01-06T06:53:15Z","_id":"17605","oa":"1"},{"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"}],"date_created":"2020-11-06T09:42:27Z","status":"public","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"publication":"Workshop MetaLearn 2020 @ NeurIPS 2020","author":[{"full_name":"Tornede, Alexander","first_name":"Alexander","id":"38209","last_name":"Tornede"},{"orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"}],"user_id":"5786","title":"Towards Meta-Algorithm Selection","language":[{"iso":"eng"}],"year":"2020","type":"conference","citation":{"ieee":"A. Tornede, M. D. Wever, and E. Hüllermeier, “Towards Meta-Algorithm Selection,” presented at the Workshop MetaLearn 2020 @ NeurIPS 2020, Online, 2020.","short":"A. Tornede, M.D. Wever, E. Hüllermeier, in: Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.","mla":"Tornede, Alexander, et al. “Towards Meta-Algorithm Selection.” Workshop MetaLearn 2020 @ NeurIPS 2020, 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} }","chicago":"Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Towards Meta-Algorithm Selection.” In Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.","ama":"Tornede A, Wever MD, Hüllermeier E. Towards Meta-Algorithm Selection. In: Workshop MetaLearn 2020 @ NeurIPS 2020. ; 2020.","apa":"Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. Workshop MetaLearn 2020 @ NeurIPS 2020. Workshop MetaLearn 2020 @ NeurIPS 2020, Online."},"conference":{"name":"Workshop MetaLearn 2020 @ NeurIPS 2020","location":"Online"},"date_updated":"2022-01-06T06:54:26Z","_id":"20306"},{"publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783030535513","9783030535520"]},"publication_status":"published","project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"title":"Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach","place":"Cham","language":[{"iso":"eng"}],"series_title":"Lecture Notes in Computer Science","doi":"10.1007/978-3-030-53552-0_22","date_updated":"2022-01-06T06:53:25Z","volume":12096,"status":"public","date_created":"2020-08-17T11:44:37Z","author":[{"first_name":"Adil","full_name":"El Mesaoudi-Paul, Adil","last_name":"El Mesaoudi-Paul"},{"first_name":"Dimitri","full_name":"Weiß, Dimitri","last_name":"Weiß"},{"id":"76599","last_name":"Bengs","full_name":"Bengs, Viktor","first_name":"Viktor"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"},{"last_name":"Tierney","first_name":"Kevin","full_name":"Tierney, Kevin"}],"publisher":"Springer","publication":"Learning and Intelligent Optimization. LION 2020.","user_id":"76599","type":"book_chapter","year":"2020","citation":{"mla":"El Mesaoudi-Paul, Adil, et al. “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach.” Learning and Intelligent Optimization. LION 2020., vol. 12096, Springer, 2020, pp. 216–32, doi:10.1007/978-3-030-53552-0_22.","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={10.1007/978-3-030-53552-0_22}, 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} }","chicago":"El Mesaoudi-Paul, Adil, Dimitri Weiß, Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach.” In Learning and Intelligent Optimization. LION 2020., 12096:216–32. Lecture Notes in Computer Science. Cham: Springer, 2020. https://doi.org/10.1007/978-3-030-53552-0_22.","apa":"El Mesaoudi-Paul, A., Weiß, D., Bengs, V., Hüllermeier, E., & Tierney, K. (2020). Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In Learning and Intelligent Optimization. LION 2020. (Vol. 12096, pp. 216–232). Cham: Springer. https://doi.org/10.1007/978-3-030-53552-0_22","ama":"El Mesaoudi-Paul A, Weiß D, Bengs V, Hüllermeier E, Tierney K. Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In: Learning and Intelligent Optimization. LION 2020. Vol 12096. Lecture Notes in Computer Science. Cham: Springer; 2020:216-232. doi:10.1007/978-3-030-53552-0_22","ieee":"A. El Mesaoudi-Paul, D. Weiß, V. Bengs, E. Hüllermeier, and K. Tierney, “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach,” in Learning and Intelligent Optimization. LION 2020., vol. 12096, Cham: Springer, 2020, pp. 216–232.","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."},"page":"216 - 232","_id":"18014","intvolume":" 12096"},{"publication_status":"draft","date_created":"2020-08-17T11:49:40Z","project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"status":"public","publication":"arXiv:2002.04275","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"author":[{"last_name":"El Mesaoudi-Paul","full_name":"El Mesaoudi-Paul, Adil","first_name":"Adil"},{"full_name":"Bengs, Viktor","first_name":"Viktor","id":"76599","last_name":"Bengs"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"title":"Online Preselection with Context Information under the Plackett-Luce Model","user_id":"76599","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."}],"year":"2020","type":"preprint","citation":{"short":"A. El Mesaoudi-Paul, V. Bengs, E. Hüllermeier, ArXiv:2002.04275 (n.d.).","ieee":"A. El Mesaoudi-Paul, V. Bengs, and E. Hüllermeier, “Online Preselection with Context Information under the Plackett-Luce Model,” arXiv:2002.04275. .","chicago":"El Mesaoudi-Paul, Adil, Viktor Bengs, and Eyke Hüllermeier. “Online Preselection with Context Information under the Plackett-Luce Model.” ArXiv:2002.04275, n.d.","apa":"El Mesaoudi-Paul, A., Bengs, V., & Hüllermeier, E. (n.d.). Online Preselection with Context Information under the Plackett-Luce Model. ArXiv:2002.04275.","ama":"El Mesaoudi-Paul A, Bengs V, Hüllermeier E. Online Preselection with Context Information under the Plackett-Luce Model. arXiv:200204275.","mla":"El Mesaoudi-Paul, Adil, et al. “Online Preselection with Context Information under the Plackett-Luce Model.” ArXiv:2002.04275.","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} }"},"language":[{"iso":"eng"}],"_id":"18017","date_updated":"2022-01-06T06:53:25Z"},{"conference":{"end_date":"2020-11-20","name":"12th Asian Conference on Machine Learning","start_date":"2020-11-18","location":"Bangkok, Thailand"},"_id":"18276","date_updated":"2022-01-06T06:53:28Z","main_file_link":[{"url":"https://arxiv.org/pdf/2007.02816.pdf"}],"language":[{"iso":"eng"}],"type":"conference","citation":{"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 ACML 2020, 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: ACML 2020. ; 2020.","apa":"Tornede, A., Wever, M. D., Werner, S., Mohr, F., & Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. ACML 2020. 12th Asian Conference on Machine Learning, Bangkok, Thailand.","mla":"Tornede, Alexander, et al. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” ACML 2020, 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} }","short":"A. Tornede, M.D. Wever, S. Werner, F. Mohr, E. Hüllermeier, in: ACML 2020, 2020.","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."},"year":"2020","abstract":[{"lang":"eng","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."}],"user_id":"5786","title":"Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"publication":"ACML 2020","author":[{"full_name":"Tornede, Alexander","first_name":"Alexander","id":"38209","last_name":"Tornede"},{"id":"33176","last_name":"Wever","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik"},{"first_name":"Stefan","full_name":"Werner, Stefan","last_name":"Werner"},{"last_name":"Mohr","first_name":"Felix","full_name":"Mohr, Felix"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"project":[{"name":"SFB 901","_id":"1"},{"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"}],"date_created":"2020-08-25T12:09:28Z","status":"public"},{"year":"2020","citation":{"short":"C. Richter, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, Journal of Automated Software Engineering (n.d.).","ieee":"C. Richter, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Algorithm Selection for Software Validation Based on Graph Kernels,” Journal of Automated Software Engineering.","chicago":"Richter, Cedric, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim. “Algorithm Selection for Software Validation Based on Graph Kernels.” Journal of Automated Software Engineering, n.d.","apa":"Richter, C., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (n.d.). Algorithm Selection for Software Validation Based on Graph Kernels. Journal of Automated Software Engineering.","ama":"Richter C, Hüllermeier E, Jakobs M-C, Wehrheim H. Algorithm Selection for Software Validation Based on Graph Kernels. Journal of Automated Software Engineering.","mla":"Richter, Cedric, et al. “Algorithm Selection for Software Validation Based on Graph Kernels.” Journal of Automated Software Engineering, Springer.","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} }"},"type":"journal_article","language":[{"iso":"eng"}],"_id":"16725","date_updated":"2022-01-06T06:52:55Z","department":[{"_id":"7"},{"_id":"77"},{"_id":"355"}],"publication":"Journal of Automated Software Engineering","author":[{"id":"50003","last_name":"Richter","full_name":"Richter, Cedric","first_name":"Cedric"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"},{"last_name":"Jakobs","first_name":"Marie-Christine","full_name":"Jakobs, Marie-Christine"},{"full_name":"Wehrheim, Heike","first_name":"Heike","id":"573","last_name":"Wehrheim"}],"publisher":"Springer","publication_status":"accepted","project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"11","name":"SFB 901 - Subproject B3"},{"name":"SFB 901 - Subproject B4","_id":"12"}],"date_created":"2020-04-19T14:08:06Z","status":"public","title":"Algorithm Selection for Software Validation Based on Graph Kernels","user_id":"477"},{"year":"2020","type":"conference","citation":{"apa":"Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (n.d.). LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Symposium on Intelligent Data Analysis, Konstanz, Germany.","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.","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.","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} }","mla":"Wever, Marcel Dominik, et al. LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Springer.","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."},"language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:52:30Z","_id":"15629","conference":{"name":"Symposium on Intelligent Data Analysis","start_date":"2020-04-24","location":"Konstanz, Germany","end_date":"2020-04-27"},"author":[{"orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"first_name":"Alexander","full_name":"Tornede, Alexander","last_name":"Tornede","id":"38209"},{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"publisher":"Springer","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"publication_status":"accepted","status":"public","date_created":"2020-01-23T08:44:08Z","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"abstract":[{"lang":"eng","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."}],"title":"LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification","user_id":"5786"},{"_id":"15025","intvolume":" 28","issue":"2","type":"journal_article","citation":{"bibtex":"@article{Wever_van Rooijen_Hamann_2020, title={Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets}, volume={28}, DOI={10.1162/evco_a_00266}, number={2}, journal={Evolutionary Computation}, publisher={MIT Press Journals}, author={Wever, Marcel Dominik and van Rooijen, Lorijn and Hamann, Heiko}, year={2020}, pages={165–193} }","mla":"Wever, Marcel Dominik, et al. “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.” Evolutionary Computation, vol. 28, no. 2, MIT Press Journals, 2020, pp. 165–193, doi:10.1162/evco_a_00266.","chicago":"Wever, Marcel Dominik, Lorijn van Rooijen, and Heiko Hamann. “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.” Evolutionary Computation 28, no. 2 (2020): 165–193. https://doi.org/10.1162/evco_a_00266.","apa":"Wever, M. D., van Rooijen, L., & Hamann, H. (2020). Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary Computation, 28(2), 165–193. https://doi.org/10.1162/evco_a_00266","ama":"Wever MD, van Rooijen L, Hamann H. Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary Computation. 2020;28(2):165–193. doi:10.1162/evco_a_00266","ieee":"M. D. Wever, L. van Rooijen, and H. Hamann, “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets,” Evolutionary Computation, vol. 28, no. 2, pp. 165–193, 2020, doi: 10.1162/evco_a_00266.","short":"M.D. Wever, L. van Rooijen, H. Hamann, Evolutionary Computation 28 (2020) 165–193."},"year":"2020","page":"165–193","abstract":[{"text":"In software engineering, the imprecise requirements of a user are transformed to a formal requirements specification during the requirements elicitation process. This process is usually guided by requirements engineers interviewing the user. We want to partially automate this first step of the software engineering process in order to enable users to specify a desired software system on their own. With our approach, users are only asked to provide exemplary behavioral descriptions. The problem of synthesizing a requirements specification from examples can partially be reduced to the problem of grammatical inference, to which we apply an active coevolutionary learning approach. However, this approach would usually require many feedback queries to be sent to the user. In this work, we extend and generalize our active learning approach to receive knowledge from multiple oracles, also known as proactive learning. The ‘user oracle’ represents input received from the user and the ‘knowledge oracle’ represents available, formalized domain knowledge. We call our two-oracle approach the ‘first apply knowledge then query’ (FAKT/Q) algorithm. We compare FAKT/Q to the active learning approach and provide an extensive benchmark evaluation. As result we find that the number of required user queries is reduced and the inference process is sped up significantly. Finally, with so-called On-The-Fly Markets, we present a motivation and an application of our approach where such knowledge is available.","lang":"eng"}],"user_id":"15415","publisher":"MIT Press Journals","author":[{"id":"33176","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik"},{"first_name":"Lorijn","full_name":"van Rooijen, Lorijn","last_name":"van Rooijen","id":"58843"},{"first_name":"Heiko","full_name":"Hamann, Heiko","last_name":"Hamann"}],"publication":"Evolutionary Computation","status":"public","date_created":"2019-11-18T14:19:19Z","volume":28,"date_updated":"2022-01-06T06:52:15Z","doi":"10.1162/evco_a_00266","language":[{"iso":"eng"}],"related_material":{"link":[{"url":"https://www.mitpressjournals.org/doi/pdf/10.1162/evco_a_00266","relation":"confirmation"}]},"title":"Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"},{"_id":"63"},{"_id":"238"}],"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"9","name":"SFB 901 - Subproject B1"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"publication_status":"published"},{"type":"preprint","citation":{"ieee":"K. Pfannschmidt, P. Gupta, and E. Hüllermeier, “Learning Choice Functions: Concepts and Architectures,” arXiv:1901.10860. 2019.","short":"K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1901.10860 (2019).","bibtex":"@article{Pfannschmidt_Gupta_Hüllermeier_2019, title={Learning Choice Functions: Concepts and Architectures}, journal={arXiv:1901.10860}, author={Pfannschmidt, Karlson and Gupta, Pritha and Hüllermeier, Eyke}, year={2019} }","mla":"Pfannschmidt, Karlson, et al. “Learning Choice Functions: Concepts and Architectures.” ArXiv:1901.10860, 2019.","chicago":"Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Learning Choice Functions: Concepts and Architectures.” ArXiv:1901.10860, 2019.","apa":"Pfannschmidt, K., Gupta, P., & Hüllermeier, E. (2019). Learning Choice Functions: Concepts and Architectures. ArXiv:1901.10860.","ama":"Pfannschmidt K, Gupta P, Hüllermeier E. Learning Choice Functions: Concepts and Architectures. arXiv:190110860. 2019."},"year":"2019","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:54:06Z","_id":"19523","date_created":"2020-09-17T10:53:38Z","project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"status":"public","publication":"arXiv:1901.10860","department":[{"_id":"7"},{"_id":"355"}],"author":[{"last_name":"Pfannschmidt","first_name":"Karlson","full_name":"Pfannschmidt, Karlson"},{"full_name":"Gupta, Pritha","first_name":"Pritha","last_name":"Gupta"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"title":"Learning Choice Functions: Concepts and Architectures","user_id":"13472","abstract":[{"text":"We study the problem of learning choice functions, which play an important\r\nrole in various domains of application, most notably in the field of economics.\r\nFormally, a choice function is a mapping from sets to sets: Given a set of\r\nchoice alternatives as input, a choice function identifies a subset of most\r\npreferred elements. Learning choice functions from suitable training data comes\r\nwith a number of challenges. For example, the sets provided as input and the\r\nsubsets produced as output can be of any size. Moreover, since the order in\r\nwhich alternatives are presented is irrelevant, a choice function should be\r\nsymmetric. Perhaps most importantly, choice functions are naturally\r\ncontext-dependent, in the sense that the preference in favor of an alternative\r\nmay depend on what other options are available. We formalize the problem of\r\nlearning choice functions and present two general approaches based on two\r\nrepresentations of context-dependent utility functions. Both approaches are\r\ninstantiated by means of appropriate neural network architectures, and their\r\nperformance is demonstrated on suitable benchmark tasks.","lang":"eng"}]},{"page":"124-146","citation":{"mla":"Merten, Marie-Luis, et al. “Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff.” Niederdeutsches Jahrbuch, no. 142, 2019, pp. 124–46.","bibtex":"@article{Merten_Seemann_Wever_2019, title={Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff}, number={142}, journal={Niederdeutsches Jahrbuch}, author={Merten, Marie-Luis and Seemann, Nina and Wever, Marcel Dominik}, year={2019}, pages={124–146} }","apa":"Merten, M.-L., Seemann, N., & Wever, M. D. (2019). Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. Niederdeutsches Jahrbuch, 142, 124–146.","ama":"Merten M-L, Seemann N, Wever MD. Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. Niederdeutsches Jahrbuch. 2019;(142):124-146.","chicago":"Merten, Marie-Luis, Nina Seemann, and Marcel Dominik Wever. “Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff.” Niederdeutsches Jahrbuch, no. 142 (2019): 124–46.","ieee":"M.-L. Merten, N. Seemann, and M. D. Wever, “Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff,” Niederdeutsches Jahrbuch, no. 142, pp. 124–146, 2019.","short":"M.-L. Merten, N. Seemann, M.D. Wever, Niederdeutsches Jahrbuch (2019) 124–146."},"year":"2019","type":"journal_article","language":[{"iso":"ger"}],"issue":"142","_id":"17565","date_updated":"2022-01-06T06:53:15Z","publication_status":"published","date_created":"2020-08-03T13:55:04Z","project":[{"_id":"39","name":"InterGramm"}],"status":"public","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"publication":"Niederdeutsches Jahrbuch","author":[{"last_name":"Merten","first_name":"Marie-Luis","full_name":"Merten, Marie-Luis"},{"full_name":"Seemann, Nina","first_name":"Nina","last_name":"Seemann"},{"id":"33176","last_name":"Wever","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik"}],"title":"Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff","user_id":"5786"},{"_id":"18018","date_updated":"2022-01-06T06:53:25Z","type":"preprint","citation":{"apa":"Bengs, V., & Holzmann, H. (2019). Uniform approximation in classical weak convergence theory. ArXiv:1903.09864.","ama":"Bengs V, Holzmann H. Uniform approximation in classical weak convergence theory. arXiv:190309864. 2019.","chicago":"Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak Convergence Theory.” ArXiv:1903.09864, 2019.","mla":"Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak Convergence Theory.” ArXiv:1903.09864, 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} }","short":"V. Bengs, H. Holzmann, ArXiv:1903.09864 (2019).","ieee":"V. Bengs and H. Holzmann, “Uniform approximation in classical weak convergence theory,” arXiv:1903.09864. 2019."},"year":"2019","title":"Uniform approximation in classical weak convergence theory","user_id":"76599","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."}],"date_created":"2020-08-17T12:10:55Z","status":"public","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"publication":"arXiv:1903.09864","author":[{"first_name":"Viktor","full_name":"Bengs, Viktor","last_name":"Bengs"},{"last_name":"Holzmann","first_name":"Hajo","full_name":"Holzmann, Hajo"}]},{"file":[{"content_type":"application/pdf","date_updated":"2019-04-10T07:17:17Z","relation":"main_file","success":1,"file_size":"74484","creator":"wever","file_id":"8870","access_level":"closed","date_created":"2019-04-10T07:17:17Z","file_name":"Towards_Automated_Machine_Learning_for_Multi_Label_Classification.pdf"}],"department":[{"_id":"355"}],"file_date_updated":"2019-04-10T07:17:17Z","author":[{"orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"},{"full_name":"Hetzer, Alexander","first_name":"Alexander","id":"38209","last_name":"Hetzer"}],"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"date_created":"2019-04-10T07:17:55Z","status":"public","has_accepted_license":"1","user_id":"49109","ddc":["000"],"title":"Towards Automated Machine Learning for Multi-Label Classification","language":[{"iso":"eng"}],"type":"conference_abstract","year":"2019","citation":{"chicago":"Wever, Marcel Dominik, Felix Mohr, Eyke Hüllermeier, and Alexander Hetzer. “Towards Automated Machine Learning for Multi-Label Classification,” 2019.","apa":"Wever, M. D., Mohr, F., Hüllermeier, E., & Hetzer, A. (2019). Towards Automated Machine Learning for Multi-Label Classification. Presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany.","ama":"Wever MD, Mohr F, Hüllermeier E, Hetzer A. Towards Automated Machine Learning for Multi-Label Classification. In: ; 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. Towards Automated Machine Learning for Multi-Label Classification. 2019.","short":"M.D. Wever, F. Mohr, E. Hüllermeier, A. Hetzer, in: 2019.","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."},"conference":{"location":"Bayreuth, Germany","start_date":"2019-03-18","name":"European Conference on Data Analytics (ECDA)","end_date":"2019-03-20"},"_id":"8868","date_updated":"2022-01-06T07:04:04Z"},{"author":[{"full_name":"Tagne, V. K.","first_name":"V. K.","last_name":"Tagne"},{"last_name":"Fotso","full_name":"Fotso, S.","first_name":"S."},{"full_name":"Fono, L. A. ","first_name":"L. A. ","last_name":"Fono"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"publication":"New Mathematics and Natural Computation","department":[{"_id":"34"},{"_id":"355"},{"_id":"7"}],"status":"public","date_created":"2019-07-08T15:34:03Z","volume":15,"user_id":"315","title":"Choice Functions Generated by Mallows and Plackett–Luce Relations","language":[{"iso":"eng"}],"type":"journal_article","citation":{"short":"V.K. Tagne, S. Fotso, L.A. Fono, E. Hüllermeier, New Mathematics and Natural Computation 15 (2019) 191–213.","ieee":"V. K. Tagne, S. Fotso, L. A. Fono, and E. Hüllermeier, “Choice Functions Generated by Mallows and Plackett–Luce Relations,” New Mathematics and Natural Computation, vol. 15, no. 2, pp. 191–213, 2019.","chicago":"Tagne, V. K., S. Fotso, L. A. Fono, and Eyke Hüllermeier. “Choice Functions Generated by Mallows and Plackett–Luce Relations.” New Mathematics and Natural Computation 15, no. 2 (2019): 191–213.","ama":"Tagne VK, Fotso S, Fono LA, Hüllermeier E. Choice Functions Generated by Mallows and Plackett–Luce Relations. New Mathematics and Natural Computation. 2019;15(2):191-213.","apa":"Tagne, V. K., Fotso, S., Fono, L. A., & Hüllermeier, E. (2019). Choice Functions Generated by Mallows and Plackett–Luce Relations. New Mathematics and Natural Computation, 15(2), 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} }","mla":"Tagne, V. K., et al. “Choice Functions Generated by Mallows and Plackett–Luce Relations.” New Mathematics and Natural Computation, vol. 15, no. 2, 2019, pp. 191–213."},"year":"2019","page":"191-213","date_updated":"2022-01-06T06:50:45Z","_id":"10578","intvolume":" 15","issue":"2"},{"publication":"IEEE Computational Intelligence Magazine","department":[{"_id":"34"},{"_id":"355"}],"author":[{"full_name":"Couso, Ines","first_name":"Ines","last_name":"Couso"},{"first_name":"Christian","full_name":"Borgelt, Christian","last_name":"Borgelt"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"},{"first_name":"Rudolf","full_name":"Kruse, Rudolf","last_name":"Kruse"}],"date_created":"2019-11-15T10:11:37Z","status":"public","publication_status":"published","publication_identifier":{"issn":["1556-603X","1556-6048"]},"user_id":"315","title":"Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning","language":[{"iso":"eng"}],"page":"31-44","citation":{"chicago":"Couso, Ines, Christian Borgelt, Eyke Hüllermeier, and Rudolf Kruse. “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning.” IEEE Computational Intelligence Magazine, 2019, 31–44. https://doi.org/10.1109/mci.2018.2881642.","apa":"Couso, I., Borgelt, C., Hüllermeier, E., & Kruse, R. (2019). Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Computational Intelligence Magazine, 31–44. https://doi.org/10.1109/mci.2018.2881642","ama":"Couso I, Borgelt C, Hüllermeier E, Kruse R. Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Computational Intelligence Magazine. 2019:31-44. doi:10.1109/mci.2018.2881642","bibtex":"@article{Couso_Borgelt_Hüllermeier_Kruse_2019, title={Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning}, DOI={10.1109/mci.2018.2881642}, journal={IEEE Computational Intelligence Magazine}, author={Couso, Ines and Borgelt, Christian and Hüllermeier, Eyke and Kruse, Rudolf}, year={2019}, pages={31–44} }","mla":"Couso, Ines, et al. “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning.” IEEE Computational Intelligence Magazine, 2019, pp. 31–44, doi:10.1109/mci.2018.2881642.","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,” IEEE Computational Intelligence Magazine, pp. 31–44, 2019."},"type":"journal_article","year":"2019","_id":"15001","date_updated":"2022-01-06T06:52:13Z","doi":"10.1109/mci.2018.2881642"},{"issue":"2","intvolume":" 33","_id":"15002","page":"293-324","citation":{"ama":"Waegeman W, Dembczynski K, Hüllermeier E. Multi-target prediction: a unifying view on problems and methods. Data Mining and Knowledge Discovery. 2019;33(2):293-324. doi:10.1007/s10618-018-0595-5","apa":"Waegeman, W., Dembczynski, K., & Hüllermeier, E. (2019). Multi-target prediction: a unifying view on problems and methods. Data Mining and Knowledge Discovery, 33(2), 293–324. https://doi.org/10.1007/s10618-018-0595-5","chicago":"Waegeman, Willem, Krzysztof Dembczynski, and Eyke Hüllermeier. “Multi-Target Prediction: A Unifying View on Problems and Methods.” Data Mining and Knowledge Discovery 33, no. 2 (2019): 293–324. https://doi.org/10.1007/s10618-018-0595-5.","mla":"Waegeman, Willem, et al. “Multi-Target Prediction: A Unifying View on Problems and Methods.” Data Mining and Knowledge Discovery, vol. 33, no. 2, 2019, pp. 293–324, doi:10.1007/s10618-018-0595-5.","bibtex":"@article{Waegeman_Dembczynski_Hüllermeier_2019, title={Multi-target prediction: a unifying view on problems and methods}, volume={33}, DOI={10.1007/s10618-018-0595-5}, number={2}, journal={Data Mining and Knowledge Discovery}, author={Waegeman, Willem and Dembczynski, Krzysztof and Hüllermeier, Eyke}, year={2019}, pages={293–324} }","short":"W. Waegeman, K. Dembczynski, E. Hüllermeier, Data Mining and Knowledge Discovery 33 (2019) 293–324.","ieee":"W. Waegeman, K. Dembczynski, and E. Hüllermeier, “Multi-target prediction: a unifying view on problems and methods,” Data Mining and Knowledge Discovery, vol. 33, no. 2, pp. 293–324, 2019."},"type":"journal_article","year":"2019","user_id":"315","ddc":["000"],"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."}],"date_created":"2019-11-15T10:16:34Z","status":"public","has_accepted_license":"1","volume":33,"file":[{"file_name":"multi-target-prediction.pdf","date_created":"2020-02-28T12:43:39Z","access_level":"open_access","file_size":837808,"file_id":"16155","creator":"lettmann","date_updated":"2020-02-28T12:45:26Z","content_type":"application/pdf","relation":"main_file"}],"file_date_updated":"2020-02-28T12:45:26Z","publication":"Data Mining and Knowledge Discovery","author":[{"last_name":"Waegeman","first_name":"Willem","full_name":"Waegeman, Willem"},{"last_name":"Dembczynski","first_name":"Krzysztof","full_name":"Dembczynski, Krzysztof"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"oa":"1","doi":"10.1007/s10618-018-0595-5","date_updated":"2022-01-06T06:52:14Z","language":[{"iso":"eng"}],"title":"Multi-target prediction: a unifying view on problems and methods","publication_identifier":{"issn":["1573-756X"]},"department":[{"_id":"34"},{"_id":"355"}]},{"date_created":"2019-11-15T10:20:55Z","status":"public","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","author":[{"full_name":"Mortier, Thomas","first_name":"Thomas","last_name":"Mortier"},{"last_name":"Wydmuch","first_name":"Marek","full_name":"Wydmuch, Marek"},{"last_name":"Dembczynski","full_name":"Dembczynski, Krzysztof","first_name":"Krzysztof"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"},{"last_name":"Waegeman","first_name":"Willem","full_name":"Waegeman, Willem"}],"user_id":"315","title":"Set-Valued Prediction in Multi-Class Classification","language":[{"iso":"eng"}],"citation":{"chicago":"Mortier, Thomas, Marek Wydmuch, Krzysztof Dembczynski, Eyke Hüllermeier, and Willem Waegeman. “Set-Valued Prediction in Multi-Class Classification.” 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., & Waegeman, W. (2019). Set-Valued Prediction in Multi-Class Classification. 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.","ama":"Mortier T, Wydmuch M, Dembczynski K, Hüllermeier E, Waegeman W. Set-Valued Prediction in Multi-Class Classification. 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.","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.” 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.","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.","ieee":"T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier, and W. 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Holzmann, “Asymptotic confidence sets for the jump curve in bivariate regression problems,” Journal of Multivariate Analysis, pp. 291–312, 2019."},"year":"2019","type":"journal_article","page":"291-312","language":[{"iso":"eng"}]},{"author":[{"first_name":"Viktor","full_name":"Bengs, Viktor","last_name":"Bengs","id":"76599"},{"last_name":"Holzmann","first_name":"Hajo","full_name":"Holzmann, Hajo"}],"publication":"Electronic Journal of Statistics","department":[{"_id":"34"},{"_id":"355"}],"publication_identifier":{"issn":["1935-7524"]},"publication_status":"published","status":"public","date_created":"2019-10-30T14:25:16Z","title":"Adaptive confidence sets for kink estimation","user_id":"76599","type":"journal_article","year":"2019","citation":{"bibtex":"@article{Bengs_Holzmann_2019, title={Adaptive confidence sets for kink estimation}, DOI={10.1214/19-ejs1555}, journal={Electronic Journal of Statistics}, author={Bengs, Viktor and Holzmann, Hajo}, year={2019}, pages={1523–1579} }","mla":"Bengs, Viktor, and Hajo Holzmann. “Adaptive Confidence Sets for Kink Estimation.” Electronic Journal of Statistics, 2019, pp. 1523–79, doi:10.1214/19-ejs1555.","apa":"Bengs, V., & Holzmann, H. (2019). Adaptive confidence sets for kink estimation. Electronic Journal of Statistics, 1523–1579. https://doi.org/10.1214/19-ejs1555","ama":"Bengs V, Holzmann H. Adaptive confidence sets for kink estimation. Electronic Journal of Statistics. 2019:1523-1579. doi:10.1214/19-ejs1555","chicago":"Bengs, Viktor, and Hajo Holzmann. “Adaptive Confidence Sets for Kink Estimation.” Electronic Journal of Statistics, 2019, 1523–79. https://doi.org/10.1214/19-ejs1555.","ieee":"V. Bengs and H. Holzmann, “Adaptive confidence sets for kink estimation,” Electronic Journal of Statistics, pp. 1523–1579, 2019.","short":"V. Bengs, H. Holzmann, Electronic Journal of Statistics (2019) 1523–1579."},"page":"1523-1579","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:51:52Z","_id":"14028","doi":"10.1214/19-ejs1555"},{"conference":{"end_date":"2019-09-26","location":"Kassel","start_date":"2019-09-23","name":"Informatik 2019"},"date_updated":"2022-01-06T06:51:28Z","_id":"13132","language":[{"iso":"eng"}],"page":" 273-274 ","type":"conference_abstract","year":"2019","citation":{"mla":"Mohr, Felix, et al. “From Automated to On-The-Fly Machine Learning.” INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, 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} }","chicago":"Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier. “From Automated to On-The-Fly Machine Learning.” In INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, 273–74. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft Für Informatik. Bonn: Gesellschaft für Informatik e.V., 2019.","ama":"Mohr F, Wever MD, Tornede A, Hüllermeier E. From Automated to On-The-Fly Machine Learning. In: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik. Bonn: Gesellschaft für Informatik e.V.; 2019:273-274.","apa":"Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (2019). From Automated to On-The-Fly Machine Learning. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (pp. 273–274). Bonn: Gesellschaft für Informatik e.V.","ieee":"F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “From Automated to On-The-Fly Machine Learning,” in INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, Kassel, 2019, pp. 273–274.","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."},"series_title":"INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik","user_id":"38209","title":"From Automated to On-The-Fly Machine Learning","place":"Bonn","date_created":"2019-09-04T08:44:46Z","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"status":"public","department":[{"_id":"355"}],"publication":"INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft","publisher":"Gesellschaft für Informatik e.V.","author":[{"last_name":"Mohr","first_name":"Felix","full_name":"Mohr, Felix"},{"orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"last_name":"Tornede","id":"38209","first_name":"Alexander","full_name":"Tornede, Alexander"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"}]},{"abstract":[{"lang":"eng","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."}],"title":"Automating Multi-Label Classification Extending ML-Plan","ddc":["006"],"user_id":"33176","author":[{"orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"id":"38209","last_name":"Tornede","full_name":"Tornede, Alexander","first_name":"Alexander"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"department":[{"_id":"355"}],"file_date_updated":"2019-09-10T08:20:44Z","file":[{"access_level":"open_access","file_name":"Automating_MultiLabel_Classification_Extending_ML-Plan.pdf","date_created":"2019-09-10T08:19:01Z","relation":"main_file","content_type":"application/pdf","date_updated":"2019-09-10T08:20:44Z","file_id":"13177","creator":"wever","file_size":388191}],"has_accepted_license":"1","status":"public","project":[{"name":"SFB 901","_id":"1"},{"_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"}],"date_created":"2019-06-11T21:33:06Z","_id":"10232","date_updated":"2022-01-06T06:50:33Z","conference":{"end_date":"2019-06-15","location":"Long Beach, CA, USA","name":"6th ICML Workshop on Automated Machine Learning (AutoML 2019)","start_date":"2019-06-09"},"oa":"1","type":"conference","year":"2019","citation":{"short":"M.D. Wever, F. Mohr, A. Tornede, E. Hüllermeier, in: 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.","chicago":"Wever, Marcel Dominik, Felix Mohr, Alexander Tornede, and Eyke Hüllermeier. “Automating Multi-Label Classification Extending ML-Plan,” 2019.","ama":"Wever MD, Mohr F, Tornede A, Hüllermeier E. Automating Multi-Label Classification Extending ML-Plan. In: ; 2019.","apa":"Wever, M. D., Mohr, F., Tornede, A., & 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.","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} }","mla":"Wever, Marcel Dominik, et al. Automating Multi-Label Classification Extending ML-Plan. 2019."},"language":[{"iso":"eng"}]},{"type":"journal_article","year":"2019","citation":{"ama":"Rohlfing K, Leonardi G, Nomikou I, Rączaszek-Leonardi J, Hüllermeier E. Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems. Published online 2019. doi:10.1109/TCDS.2019.2892991","apa":"Rohlfing, K., Leonardi, G., Nomikou, I., Rączaszek-Leonardi, J., & Hüllermeier, E. (2019). Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems. https://doi.org/10.1109/TCDS.2019.2892991","chicago":"Rohlfing, Katharina, Giuseppe Leonardi, Iris Nomikou, Joanna Rączaszek-Leonardi, and Eyke Hüllermeier. “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches.” IEEE Transactions on Cognitive and Developmental Systems, 2019. https://doi.org/10.1109/TCDS.2019.2892991.","mla":"Rohlfing, Katharina, et al. “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches.” IEEE Transactions on Cognitive and Developmental Systems, 2019, doi:10.1109/TCDS.2019.2892991.","bibtex":"@article{Rohlfing_Leonardi_Nomikou_Rączaszek-Leonardi_Hüllermeier_2019, title={Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches}, DOI={10.1109/TCDS.2019.2892991}, 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} }","short":"K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, E. Hüllermeier, IEEE Transactions on Cognitive and Developmental Systems (2019).","ieee":"K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, and E. Hüllermeier, “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches,” IEEE Transactions on Cognitive and Developmental Systems, 2019, doi: 10.1109/TCDS.2019.2892991."},"language":[{"iso":"eng"}],"_id":"20243","date_updated":"2023-02-01T12:39:19Z","doi":"10.1109/TCDS.2019.2892991","author":[{"full_name":"Rohlfing, Katharina","first_name":"Katharina","id":"50352","last_name":"Rohlfing"},{"last_name":"Leonardi","full_name":"Leonardi, Giuseppe","first_name":"Giuseppe"},{"last_name":"Nomikou","full_name":"Nomikou, Iris","first_name":"Iris"},{"first_name":"Joanna","full_name":"Rączaszek-Leonardi, Joanna","last_name":"Rączaszek-Leonardi"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"}],"publication":"IEEE Transactions on Cognitive and Developmental Systems","department":[{"_id":"749"},{"_id":"355"}],"status":"public","date_created":"2020-11-02T13:25:49Z","title":"Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches","user_id":"14931"},{"date_updated":"2022-01-06T06:56:35Z","doi":"10.1109/SCC.2018.00039","oa":"1","language":[{"iso":"eng"}],"place":"San Francisco, CA, USA","title":"(WIP) Towards the Automated Composition of Machine Learning Services","department":[{"_id":"355"}],"publication_status":"published","project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"_id":"2479","conference":{"end_date":"2018-07-07","start_date":"2018-07-02","name":"IEEE International Conference on Services Computing, SCC 2018","location":"San Francisco, CA, USA"},"main_file_link":[{"open_access":"1","url":"https://ieeexplore.ieee.org/document/8456425"}],"year":"2018","citation":{"bibtex":"@inproceedings{Mohr_Wever_Hüllermeier_Faez_2018, place={San Francisco, CA, USA}, title={(WIP) Towards the Automated Composition of Machine Learning Services}, DOI={10.1109/SCC.2018.00039}, booktitle={SCC}, publisher={IEEE}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke and Faez, Amin}, year={2018} }","mla":"Mohr, Felix, et al. “(WIP) Towards the Automated Composition of Machine Learning Services.” SCC, IEEE, 2018, doi:10.1109/SCC.2018.00039.","ama":"Mohr F, Wever MD, Hüllermeier E, Faez A. (WIP) Towards the Automated Composition of Machine Learning Services. In: SCC. San Francisco, CA, USA: IEEE; 2018. doi:10.1109/SCC.2018.00039","apa":"Mohr, F., Wever, M. D., Hüllermeier, E., & Faez, A. (2018). (WIP) Towards the Automated Composition of Machine Learning Services. In SCC. San Francisco, CA, USA: IEEE. https://doi.org/10.1109/SCC.2018.00039","chicago":"Mohr, Felix, Marcel Dominik Wever, Eyke Hüllermeier, and Amin Faez. “(WIP) Towards the Automated Composition of Machine Learning Services.” In SCC. San Francisco, CA, USA: IEEE, 2018. https://doi.org/10.1109/SCC.2018.00039.","ieee":"F. Mohr, M. D. Wever, E. Hüllermeier, and A. Faez, “(WIP) Towards the Automated Composition of Machine Learning Services,” in SCC, San Francisco, CA, USA, 2018.","short":"F. Mohr, M.D. Wever, E. Hüllermeier, A. Faez, in: SCC, IEEE, San Francisco, CA, USA, 2018."},"type":"conference","ddc":["000"],"user_id":"49109","publisher":"IEEE","author":[{"last_name":"Mohr","first_name":"Felix","full_name":"Mohr, Felix"},{"orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"},{"full_name":"Faez, Amin","first_name":"Amin","last_name":"Faez"}],"publication":"SCC","file_date_updated":"2018-11-06T15:08:39Z","file":[{"content_type":"application/pdf","date_updated":"2018-11-06T15:08:39Z","relation":"main_file","file_size":237890,"creator":"wever","file_id":"5382","access_level":"closed","file_name":"08456425.pdf","date_created":"2018-11-06T15:08:39Z"}],"has_accepted_license":"1","status":"public","date_created":"2018-04-24T08:34:52Z"},{"user_id":"13472","title":"Deep Architectures for Learning Context-dependent Ranking Functions","abstract":[{"text":"Object ranking is an important problem in the realm of preference learning.\r\nOn the basis of training data in the form of a set of rankings of objects,\r\nwhich are typically represented as feature vectors, the goal is to learn a\r\nranking function that predicts a linear order of any new set of objects.\r\nCurrent approaches commonly focus on ranking by scoring, i.e., on learning an\r\nunderlying latent utility function that seeks to capture the inherent utility\r\nof each object. These approaches, however, are not able to take possible\r\neffects of context-dependence into account, where context-dependence means that\r\nthe utility or usefulness of an object may also depend on what other objects\r\nare available as alternatives. In this paper, we formalize the problem of\r\ncontext-dependent ranking and present two general approaches based on two\r\nnatural representations of context-dependent ranking functions. Both approaches\r\nare instantiated by means of appropriate neural network architectures, which\r\nare evaluated on suitable benchmark task.","lang":"eng"}],"status":"public","date_created":"2020-09-17T10:53:39Z","project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"author":[{"last_name":"Pfannschmidt","first_name":"Karlson","full_name":"Pfannschmidt, Karlson"},{"full_name":"Gupta, Pritha","first_name":"Pritha","last_name":"Gupta"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"publication":"arXiv:1803.05796","department":[{"_id":"7"},{"_id":"355"}],"date_updated":"2022-01-06T06:54:06Z","_id":"19524","language":[{"iso":"eng"}],"type":"preprint","year":"2018","citation":{"ieee":"K. Pfannschmidt, P. Gupta, and E. Hüllermeier, “Deep Architectures for Learning Context-dependent Ranking Functions,” arXiv:1803.05796. 2018.","short":"K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1803.05796 (2018).","bibtex":"@article{Pfannschmidt_Gupta_Hüllermeier_2018, title={Deep Architectures for Learning Context-dependent Ranking Functions}, journal={arXiv:1803.05796}, author={Pfannschmidt, Karlson and Gupta, Pritha and Hüllermeier, Eyke}, year={2018} }","mla":"Pfannschmidt, Karlson, et al. “Deep Architectures for Learning Context-Dependent Ranking Functions.” ArXiv:1803.05796, 2018.","ama":"Pfannschmidt K, Gupta P, Hüllermeier E. Deep Architectures for Learning Context-dependent Ranking Functions. arXiv:180305796. 2018.","apa":"Pfannschmidt, K., Gupta, P., & Hüllermeier, E. (2018). Deep Architectures for Learning Context-dependent Ranking Functions. ArXiv:1803.05796.","chicago":"Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Deep Architectures for Learning Context-Dependent Ranking Functions.” ArXiv:1803.05796, 2018."}},{"language":[{"iso":"eng"}],"oa":"1","date_updated":"2022-01-06T06:58:08Z","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"department":[{"_id":"355"}],"title":"Programmatic Task Network Planning","type":"conference","citation":{"short":"F. Mohr, T. Lettmann, E. Hüllermeier, M.D. Wever, in: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39.","ieee":"F. Mohr, T. Lettmann, E. Hüllermeier, and M. D. Wever, “Programmatic Task Network Planning,” in Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, Delft, Netherlands, 2018, pp. 31–39.","chicago":"Mohr, Felix, Theodor Lettmann, Eyke Hüllermeier, and Marcel Dominik Wever. “Programmatic Task Network Planning.” In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, 31–39. AAAI, 2018.","apa":"Mohr, F., Lettmann, T., Hüllermeier, E., & Wever, M. D. (2018). Programmatic Task Network Planning. In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (pp. 31–39). Delft, Netherlands: AAAI.","ama":"Mohr F, Lettmann T, Hüllermeier E, Wever MD. Programmatic Task Network Planning. In: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning. AAAI; 2018:31-39.","bibtex":"@inproceedings{Mohr_Lettmann_Hüllermeier_Wever_2018, title={Programmatic Task Network Planning}, booktitle={Proceedings of the 1st ICAPS Workshop on Hierarchical Planning}, publisher={AAAI}, author={Mohr, Felix and Lettmann, Theodor and Hüllermeier, Eyke and Wever, Marcel Dominik}, year={2018}, pages={31–39} }","mla":"Mohr, Felix, et al. “Programmatic Task Network Planning.” Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39."},"year":"2018","page":"31-39","main_file_link":[{"url":"http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop08/docs/Mohr18ProgrammaticPlanning.pdf","open_access":"1"}],"_id":"2857","conference":{"start_date":"2018-06-24","name":"28th International Conference on Automated Planning and Scheduling","location":"Delft, Netherlands","end_date":"2018-06-29"},"has_accepted_license":"1","status":"public","date_created":"2018-05-24T09:00:20Z","author":[{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"full_name":"Lettmann, Theodor","orcid":"0000-0001-5859-2457","first_name":"Theodor","id":"315","last_name":"Lettmann"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"},{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818"}],"publisher":"AAAI","publication":"Proceedings of the 1st ICAPS Workshop on Hierarchical Planning","file_date_updated":"2018-11-06T15:18:26Z","file":[{"date_created":"2018-11-06T15:18:26Z","file_name":"Mohr18ProgrammaticPlanning.pdf","access_level":"closed","file_id":"5384","creator":"wever","file_size":349958,"relation":"main_file","success":1,"date_updated":"2018-11-06T15:18:26Z","content_type":"application/pdf"}],"ddc":["000"],"user_id":"315"},{"title":"Stability of stochastic approximations with “controlled markov” noise and temporal difference learning","user_id":"66937","volume":64,"status":"public","date_created":"2021-09-10T10:17:54Z","publisher":"IEEE","author":[{"last_name":"Ramaswamy","id":"66937","first_name":"Arunselvan","orcid":"https://orcid.org/ 0000-0001-7547-8111","full_name":"Ramaswamy, Arunselvan"},{"first_name":"Shalabh","full_name":"Bhatnagar, Shalabh","last_name":"Bhatnagar"}],"department":[{"_id":"355"}],"publication":"IEEE Transactions on Automatic Control","issue":"6","intvolume":" 64","_id":"24150","date_updated":"2022-01-06T06:56:08Z","citation":{"short":"A. Ramaswamy, S. Bhatnagar, IEEE Transactions on Automatic Control 64 (2018) 2614–2620.","ieee":"A. Ramaswamy and S. Bhatnagar, “Stability of stochastic approximations with ‘controlled markov’ noise and temporal difference learning,” IEEE Transactions on Automatic Control, vol. 64, no. 6, pp. 2614–2620, 2018.","chicago":"Ramaswamy, Arunselvan, and Shalabh Bhatnagar. “Stability of Stochastic Approximations with ‘Controlled Markov’ Noise and Temporal Difference Learning.” IEEE Transactions on Automatic Control 64, no. 6 (2018): 2614–20.","apa":"Ramaswamy, A., & Bhatnagar, S. (2018). Stability of stochastic approximations with “controlled markov” noise and temporal difference learning. IEEE Transactions on Automatic Control, 64(6), 2614–2620.","ama":"Ramaswamy A, Bhatnagar S. Stability of stochastic approximations with “controlled markov” noise and temporal difference learning. IEEE Transactions on Automatic Control. 2018;64(6):2614-2620.","bibtex":"@article{Ramaswamy_Bhatnagar_2018, title={Stability of stochastic approximations with “controlled markov” noise and temporal difference learning}, volume={64}, number={6}, journal={IEEE Transactions on Automatic Control}, publisher={IEEE}, author={Ramaswamy, Arunselvan and Bhatnagar, Shalabh}, year={2018}, pages={2614–2620} }","mla":"Ramaswamy, Arunselvan, and Shalabh Bhatnagar. “Stability of Stochastic Approximations with ‘Controlled Markov’ Noise and Temporal Difference Learning.” IEEE Transactions on Automatic Control, vol. 64, no. 6, IEEE, 2018, pp. 2614–20."},"year":"2018","type":"journal_article","page":"2614-2620","language":[{"iso":"eng"}]},{"title":"Deepcas: A deep reinforcement learning algorithm for control-aware scheduling","user_id":"66937","volume":2,"date_created":"2021-09-10T10:19:07Z","status":"public","department":[{"_id":"355"}],"publication":"IEEE Control Systems Letters","author":[{"full_name":"Demirel, Burak","first_name":"Burak","last_name":"Demirel"},{"last_name":"Ramaswamy","id":"66937","first_name":"Arunselvan","orcid":"https://orcid.org/ 0000-0001-7547-8111","full_name":"Ramaswamy, Arunselvan"},{"first_name":"Daniel E","full_name":"Quevedo, Daniel E","last_name":"Quevedo"},{"full_name":"Karl, Holger","first_name":"Holger","last_name":"Karl"}],"publisher":"IEEE","issue":"4","_id":"24151","date_updated":"2022-01-06T06:56:08Z","intvolume":" 2","page":"737-742","type":"journal_article","year":"2018","citation":{"short":"B. Demirel, A. Ramaswamy, D.E. Quevedo, H. Karl, IEEE Control Systems Letters 2 (2018) 737–742.","ieee":"B. Demirel, A. Ramaswamy, D. E. Quevedo, and H. Karl, “Deepcas: A deep reinforcement learning algorithm for control-aware scheduling,” IEEE Control Systems Letters, vol. 2, no. 4, pp. 737–742, 2018.","ama":"Demirel B, Ramaswamy A, Quevedo DE, Karl H. Deepcas: A deep reinforcement learning algorithm for control-aware scheduling. IEEE Control Systems Letters. 2018;2(4):737-742.","apa":"Demirel, B., Ramaswamy, A., Quevedo, D. E., & Karl, H. (2018). Deepcas: A deep reinforcement learning algorithm for control-aware scheduling. IEEE Control Systems Letters, 2(4), 737–742.","chicago":"Demirel, Burak, Arunselvan Ramaswamy, Daniel E Quevedo, and Holger Karl. “Deepcas: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling.” IEEE Control Systems Letters 2, no. 4 (2018): 737–42.","mla":"Demirel, Burak, et al. “Deepcas: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling.” IEEE Control Systems Letters, vol. 2, no. 4, IEEE, 2018, pp. 737–42.","bibtex":"@article{Demirel_Ramaswamy_Quevedo_Karl_2018, title={Deepcas: A deep reinforcement learning algorithm for control-aware scheduling}, volume={2}, number={4}, journal={IEEE Control Systems Letters}, publisher={IEEE}, author={Demirel, Burak and Ramaswamy, Arunselvan and Quevedo, Daniel E and Karl, Holger}, year={2018}, pages={737–742} }"},"language":[{"iso":"eng"}]},{"_id":"2471","conference":{"location":"San Francisco, CA, USA","name":"IEEE International Conference on Services Computing, SCC 2018","start_date":"2018-07-02","end_date":"2018-07-07"},"year":"2018","citation":{"chicago":"Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “On-The-Fly Service Construction with Prototypes.” In SCC. San Francisco, CA, USA: IEEE Computer Society, 2018. https://doi.org/10.1109/SCC.2018.00036.","ama":"Mohr F, Wever MD, Hüllermeier E. On-The-Fly Service Construction with Prototypes. In: SCC. San Francisco, CA, USA: IEEE Computer Society; 2018. doi:10.1109/SCC.2018.00036","apa":"Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). On-The-Fly Service Construction with Prototypes. In SCC. San Francisco, CA, USA: IEEE Computer Society. https://doi.org/10.1109/SCC.2018.00036","mla":"Mohr, Felix, et al. “On-The-Fly Service Construction with Prototypes.” SCC, IEEE Computer Society, 2018, doi:10.1109/SCC.2018.00036.","bibtex":"@inproceedings{Mohr_Wever_Hüllermeier_2018, place={San Francisco, CA, USA}, title={On-The-Fly Service Construction with Prototypes}, DOI={10.1109/SCC.2018.00036}, booktitle={SCC}, publisher={IEEE Computer Society}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2018} }","short":"F. Mohr, M.D. Wever, E. Hüllermeier, in: SCC, IEEE Computer Society, San Francisco, CA, USA, 2018.","ieee":"F. Mohr, M. D. Wever, and E. Hüllermeier, “On-The-Fly Service Construction with Prototypes,” in SCC, San Francisco, CA, USA, 2018."},"type":"conference","main_file_link":[{"url":"https://ieeexplore.ieee.org/abstract/document/8456422","open_access":"1"}],"user_id":"49109","ddc":["000"],"has_accepted_license":"1","status":"public","date_created":"2018-04-23T11:40:20Z","file":[{"access_level":"closed","file_name":"08456422.pdf","date_created":"2018-11-06T15:15:38Z","success":1,"relation":"main_file","date_updated":"2018-11-06T15:15:38Z","content_type":"application/pdf","file_id":"5383","creator":"wever","file_size":356132}],"author":[{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"publisher":"IEEE Computer Society","publication":"SCC","file_date_updated":"2018-11-06T15:15:38Z","oa":"1","doi":"10.1109/SCC.2018.00036","date_updated":"2022-01-06T06:56:32Z","language":[{"iso":"eng"}],"title":"On-The-Fly Service Construction with Prototypes","place":"San Francisco, CA, USA","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"department":[{"_id":"355"}]},{"citation":{"apa":"Melnikov, V., & Hüllermeier, E. (2018). On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning. https://doi.org/10.1007/s10994-018-5733-1","ama":"Melnikov V, Hüllermeier E. On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning. 2018. doi:10.1007/s10994-018-5733-1","chicago":"Melnikov, Vitalik, and Eyke Hüllermeier. “On the Effectiveness of Heuristics for Learning Nested Dichotomies: An Empirical Analysis.” Machine Learning, 2018. https://doi.org/10.1007/s10994-018-5733-1.","bibtex":"@article{Melnikov_Hüllermeier_2018, title={On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis}, DOI={10.1007/s10994-018-5733-1}, journal={Machine Learning}, author={Melnikov, Vitalik and Hüllermeier, Eyke}, year={2018} }","mla":"Melnikov, Vitalik, and Eyke Hüllermeier. “On the Effectiveness of Heuristics for Learning Nested Dichotomies: An Empirical Analysis.” Machine Learning, 2018, doi:10.1007/s10994-018-5733-1.","short":"V. Melnikov, E. Hüllermeier, Machine Learning (2018).","ieee":"V. Melnikov and E. Hüllermeier, “On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis,” Machine Learning, 2018."},"type":"journal_article","year":"2018","_id":"3402","publication":"Machine Learning","file_date_updated":"2018-11-02T15:30:57Z","author":[{"last_name":"Melnikov","full_name":"Melnikov, Vitalik","first_name":"Vitalik"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"file":[{"date_created":"2018-11-02T15:30:57Z","file_name":"OnTheEffectivenessOfHeuristics.pdf","access_level":"closed","creator":"ups","file_id":"5305","file_size":1482882,"success":1,"relation":"main_file","date_updated":"2018-11-02T15:30:57Z","content_type":"application/pdf"}],"date_created":"2018-06-29T07:44:26Z","status":"public","has_accepted_license":"1","abstract":[{"lang":"eng","text":"In machine learning, so-called nested dichotomies are utilized as a reduction technique, i.e., to decompose a multi-class classification problem into a set of binary problems, which are solved using a simple binary classifier as a base learner. The performance of the (multi-class) classifier thus produced strongly depends on the structure of the decomposition. In this paper, we conduct an empirical study, in which we compare existing heuristics for selecting a suitable structure in the form of a nested dichotomy. Moreover, we propose two additional heuristics as natural completions. One of them is the Best-of-K heuristic, which picks the (presumably) best among K randomly generated nested dichotomies. Surprisingly, and in spite of its simplicity, it turns out to outperform the state of the art."}],"ddc":["000"],"user_id":"15504","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:59:14Z","doi":"10.1007/s10994-018-5733-1","department":[{"_id":"355"}],"publication_identifier":{"issn":["1573-0565"]},"project":[{"name":"SFB 901 - Subproject B3","_id":"11"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901","_id":"1"}],"title":"On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis"},{"department":[{"_id":"355"},{"_id":"34"},{"_id":"7"},{"_id":"26"}],"publication_status":"epub_ahead","publication_identifier":{"issn":["0885-6125"],"eissn":["1573-0565"]},"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"}],"title":"ML-Plan: Automated Machine Learning via Hierarchical Planning","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:59:21Z","doi":"10.1007/s10994-018-5735-z","oa":"1","author":[{"last_name":"Mohr","first_name":"Felix","full_name":"Mohr, Felix"},{"full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"}],"publisher":"Springer","file_date_updated":"2018-11-02T15:32:16Z","publication":"Machine Learning","keyword":["AutoML","Hierarchical Planning","HTN planning","ML-Plan"],"file":[{"file_id":"5306","creator":"ups","file_size":1070937,"success":1,"relation":"main_file","content_type":"application/pdf","date_updated":"2018-11-02T15:32:16Z","file_name":"ML-PlanAutomatedMachineLearnin.pdf","date_created":"2018-11-02T15:32:16Z","access_level":"closed"}],"has_accepted_license":"1","status":"public","date_created":"2018-07-08T14:06:14Z","article_type":"original","abstract":[{"text":"Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches.","lang":"eng"}],"ddc":["000"],"user_id":"5786","main_file_link":[{"url":"https://rdcu.be/3Nc2","open_access":"1"}],"citation":{"short":"F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515.","ieee":"F. Mohr, M. D. Wever, and E. Hüllermeier, “ML-Plan: Automated Machine Learning via Hierarchical Planning,” Machine Learning, pp. 1495–1515, 2018, doi: 10.1007/s10994-018-5735-z.","ama":"Mohr F, Wever MD, Hüllermeier E. ML-Plan: Automated Machine Learning via Hierarchical Planning. Machine Learning. Published online 2018:1495-1515. doi:10.1007/s10994-018-5735-z","apa":"Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). ML-Plan: Automated Machine Learning via Hierarchical Planning. Machine Learning, 1495–1515. https://doi.org/10.1007/s10994-018-5735-z","chicago":"Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “ML-Plan: Automated Machine Learning via Hierarchical Planning.” Machine Learning, 2018, 1495–1515. https://doi.org/10.1007/s10994-018-5735-z.","mla":"Mohr, Felix, et al. “ML-Plan: Automated Machine Learning via Hierarchical Planning.” Machine Learning, Springer, 2018, pp. 1495–515, doi:10.1007/s10994-018-5735-z.","bibtex":"@article{Mohr_Wever_Hüllermeier_2018, title={ML-Plan: Automated Machine Learning via Hierarchical Planning}, DOI={10.1007/s10994-018-5735-z}, journal={Machine Learning}, publisher={Springer}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2018}, pages={1495–1515} }"},"type":"journal_article","year":"2018","page":"1495-1515","_id":"3510","conference":{"end_date":"2018-09-14","location":"Dublin, Ireland","name":"European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases","start_date":"2018-09-10"}},{"author":[{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"quality_controlled":"1","publication":"Proceedings of the Symposium on Intelligent Data Analysis","file_date_updated":"2018-11-06T15:23:02Z","file":[{"access_level":"closed","file_name":"Mohr2018_Chapter_ReductionStumpsForMulti-classC.pdf","date_created":"2018-11-06T15:23:02Z","success":1,"relation":"main_file","date_updated":"2018-11-06T15:23:02Z","content_type":"application/pdf","creator":"wever","file_id":"5385","file_size":1348768}],"has_accepted_license":"1","status":"public","date_created":"2018-07-13T15:29:15Z","ddc":["000"],"user_id":"49109","main_file_link":[{"url":"https://link.springer.com/chapter/10.1007%2F978-3-030-01768-2_19","open_access":"1"}],"type":"conference","citation":{"short":"F. Mohr, M.D. Wever, E. Hüllermeier, in: Proceedings of the Symposium on Intelligent Data Analysis, ‘s-Hertogenbosch, the Netherlands, n.d.","ieee":"F. Mohr, M. D. Wever, and E. Hüllermeier, “Reduction Stumps for Multi-Class Classification,” in Proceedings of the Symposium on Intelligent Data Analysis, ‘s-Hertogenbosch, the Netherlands.","apa":"Mohr, F., Wever, M. D., & Hüllermeier, E. (n.d.). Reduction Stumps for Multi-Class Classification. In Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands. https://doi.org/10.1007/978-3-030-01768-2_19","ama":"Mohr F, Wever MD, Hüllermeier E. Reduction Stumps for Multi-Class Classification. In: Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands. doi:10.1007/978-3-030-01768-2_19","chicago":"Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “Reduction Stumps for Multi-Class Classification.” In Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands, n.d. https://doi.org/10.1007/978-3-030-01768-2_19.","mla":"Mohr, Felix, et al. “Reduction Stumps for Multi-Class Classification.” Proceedings of the Symposium on Intelligent Data Analysis, doi:10.1007/978-3-030-01768-2_19.","bibtex":"@inproceedings{Mohr_Wever_Hüllermeier, place={‘s-Hertogenbosch, the Netherlands}, title={Reduction Stumps for Multi-Class Classification}, DOI={10.1007/978-3-030-01768-2_19}, booktitle={Proceedings of the Symposium on Intelligent Data Analysis}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke} }"},"year":"2018","_id":"3552","conference":{"location":"‘s-Hertogenbosch, the Netherlands","start_date":"2018-10-24","name":"Symposium on Intelligent Data Analysis","end_date":"2018-10-26"},"department":[{"_id":"355"}],"publication_status":"accepted","project":[{"name":"SFB 901","_id":"1"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"3","name":"SFB 901 - Project Area B"}],"place":"‘s-Hertogenbosch, the Netherlands","title":"Reduction Stumps for Multi-Class Classification","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:59:25Z","doi":"10.1007/978-3-030-01768-2_19","oa":"1"},{"_id":"3852","urn":"38527","conference":{"location":"Stockholm, Sweden","start_date":"2018-07-10","name":"ICML 2018 AutoML Workshop","end_date":"2018-07-15"},"year":"2018","citation":{"chicago":"Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” In ICML 2018 AutoML Workshop, 2018.","apa":"Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). ML-Plan for Unlimited-Length Machine Learning Pipelines. In ICML 2018 AutoML Workshop. Stockholm, Sweden.","ama":"Wever MD, Mohr F, Hüllermeier E. ML-Plan for Unlimited-Length Machine Learning Pipelines. In: ICML 2018 AutoML Workshop. ; 2018.","bibtex":"@inproceedings{Wever_Mohr_Hüllermeier_2018, title={ML-Plan for Unlimited-Length Machine Learning Pipelines}, booktitle={ICML 2018 AutoML Workshop}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }","mla":"Wever, Marcel Dominik, et al. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” ICML 2018 AutoML Workshop, 2018.","short":"M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 2018.","ieee":"M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine Learning Pipelines,” in ICML 2018 AutoML Workshop, Stockholm, Sweden, 2018."},"type":"conference","main_file_link":[{"url":"https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE4aWNtbHxneDo3M2Q3MjUzYjViNDRhZTAx"}],"user_id":"49109","ddc":["006"],"abstract":[{"text":"In automated machine learning (AutoML), the process of engineering machine learning applications with respect to a specific problem is (partially) automated.\r\nVarious AutoML tools have already been introduced to provide out-of-the-box machine learning functionality.\r\nMore specifically, by selecting machine learning algorithms and optimizing their hyperparameters, these tools produce a machine learning pipeline tailored to the problem at hand.\r\nExcept for TPOT, all of these tools restrict the maximum number of processing steps of such a pipeline.\r\nHowever, as TPOT follows an evolutionary approach, it suffers from performance issues when dealing with larger datasets.\r\nIn this paper, we present an alternative approach leveraging a hierarchical planning to configure machine learning pipelines that are unlimited in length.\r\nWe evaluate our approach and find its performance to be competitive with other AutoML tools, including TPOT.","lang":"eng"}],"has_accepted_license":"1","status":"public","date_created":"2018-08-09T06:14:54Z","file":[{"access_level":"open_access","file_name":"38.pdf","date_created":"2018-08-09T06:14:43Z","relation":"main_file","date_updated":"2018-08-09T06:14:43Z","content_type":"application/pdf","creator":"wever","file_id":"3853","file_size":297811}],"author":[{"first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","last_name":"Wever","id":"33176"},{"last_name":"Mohr","first_name":"Felix","full_name":"Mohr, Felix"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"}],"quality_controlled":"1","file_date_updated":"2018-08-09T06:14:43Z","publication":"ICML 2018 AutoML Workshop","keyword":["automated machine learning","complex pipelines","hierarchical planning"],"oa":"1","date_updated":"2022-01-06T06:59:46Z","language":[{"iso":"eng"}],"title":"ML-Plan for Unlimited-Length Machine Learning Pipelines","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"department":[{"_id":"355"}]},{"date_updated":"2022-01-06T06:54:45Z","doi":"10.1145/3205455.3205562","oa":"1","language":[{"iso":"eng"}],"place":"Kyoto, Japan","title":"Ensembles of Evolved Nested Dichotomies for Classification","department":[{"_id":"355"}],"publication_status":"published","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"conference":{"name":"GECCO 2018","start_date":"2018-07-15","location":"Kyoto, Japan","end_date":"2018-07-19"},"_id":"2109","main_file_link":[{"url":"https://dl.acm.org/citation.cfm?doid=3205455.3205562","open_access":"1"}],"citation":{"apa":"Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). Ensembles of Evolved Nested Dichotomies for Classification. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM. https://doi.org/10.1145/3205455.3205562","ama":"Wever MD, Mohr F, Hüllermeier E. Ensembles of Evolved Nested Dichotomies for Classification. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM; 2018. doi:10.1145/3205455.3205562","chicago":"Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Ensembles of Evolved Nested Dichotomies for Classification.” In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM, 2018. https://doi.org/10.1145/3205455.3205562.","mla":"Wever, Marcel Dominik, et al. “Ensembles of Evolved Nested Dichotomies for Classification.” Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, 2018, doi:10.1145/3205455.3205562.","bibtex":"@inproceedings{Wever_Mohr_Hüllermeier_2018, place={Kyoto, Japan}, title={Ensembles of Evolved Nested Dichotomies for Classification}, DOI={10.1145/3205455.3205562}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018}, publisher={ACM}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }","short":"M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, Kyoto, Japan, 2018.","ieee":"M. D. Wever, F. Mohr, and E. Hüllermeier, “Ensembles of Evolved Nested Dichotomies for Classification,” in Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, Kyoto, Japan, 2018."},"type":"conference","year":"2018","abstract":[{"lang":"eng","text":"In multinomial classification, reduction techniques are commonly used to decompose the original learning problem into several simpler problems. For example, by recursively bisecting the original set of classes, so-called nested dichotomies define a set of binary classification problems that are organized in the structure of a binary tree. In contrast to the existing one-shot heuristics for constructing nested dichotomies and motivated by recent work on algorithm configuration, we propose a genetic algorithm for optimizing the structure of such dichotomies. 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