--- _id: '17605' 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." author: - first_name: Stefan Helmut full_name: Heid, Stefan Helmut id: '39640' last_name: Heid orcid: 0000-0002-9461-7372 - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: 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. 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} }' 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. 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. 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. short: S.H. Heid, M.D. Wever, E. Hüllermeier, Journal of Data Mining and Digital Humanities (n.d.). date_created: 2020-08-05T06:52:53Z date_updated: 2022-01-06T06:53:15Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2008.01377 oa: '1' project: - _id: '39' name: InterGramm publication: Journal of Data Mining and Digital Humanities publication_status: submitted publisher: episciences status: public title: Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction type: preprint user_id: '5786' year: '2020' ... --- _id: '20306' author: - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: 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. 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. ieee: A. Tornede, M. D. Wever, and E. Hüllermeier, “Towards Meta-Algorithm Selection,” presented at the Workshop MetaLearn 2020 @ NeurIPS 2020, Online, 2020. mla: Tornede, Alexander, et al. “Towards Meta-Algorithm Selection.” Workshop MetaLearn 2020 @ NeurIPS 2020, 2020. short: 'A. Tornede, M.D. Wever, E. Hüllermeier, in: Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.' conference: location: Online name: Workshop MetaLearn 2020 @ NeurIPS 2020 date_created: 2020-11-06T09:42:27Z date_updated: 2022-01-06T06:54:26Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: Workshop MetaLearn 2020 @ NeurIPS 2020 status: public title: Towards Meta-Algorithm Selection type: conference user_id: '5786' year: '2020' ... --- _id: '18014' 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 - first_name: Viktor full_name: Bengs, Viktor id: '76599' last_name: Bengs - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Kevin full_name: Tierney, Kevin last_name: Tierney citation: 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' 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' 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.' 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.' 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.' 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.' date_created: 2020-08-17T11:44:37Z date_updated: 2022-01-06T06:53:25Z department: - _id: '34' - _id: '7' - _id: '355' doi: 10.1007/978-3-030-53552-0_22 intvolume: ' 12096' language: - iso: eng page: 216 - 232 place: Cham project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: Learning and Intelligent Optimization. LION 2020. publication_identifier: isbn: - '9783030535513' - '9783030535520' issn: - 0302-9743 - 1611-3349 publication_status: published publisher: Springer series_title: Lecture Notes in Computer Science status: public title: 'Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach' type: book_chapter user_id: '76599' volume: 12096 year: '2020' ... --- _id: '18017' 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." author: - first_name: Adil full_name: El Mesaoudi-Paul, Adil last_name: El Mesaoudi-Paul - first_name: Viktor full_name: Bengs, Viktor id: '76599' last_name: Bengs - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: El Mesaoudi-Paul A, Bengs V, Hüllermeier E. Online Preselection with Context Information under the Plackett-Luce  Model. arXiv:200204275. apa: El Mesaoudi-Paul, A., Bengs, V., & Hüllermeier, E. (n.d.). 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} }' 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. ieee: A. El Mesaoudi-Paul, V. Bengs, and E. Hüllermeier, “Online Preselection with Context Information under the Plackett-Luce  Model,” arXiv:2002.04275. . mla: El Mesaoudi-Paul, Adil, et al. “Online Preselection with Context Information under the Plackett-Luce  Model.” ArXiv:2002.04275. short: A. El Mesaoudi-Paul, V. Bengs, E. Hüllermeier, ArXiv:2002.04275 (n.d.). date_created: 2020-08-17T11:49:40Z date_updated: 2022-01-06T06:53:25Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: arXiv:2002.04275 publication_status: draft status: public title: Online Preselection with Context Information under the Plackett-Luce Model type: preprint user_id: '76599' year: '2020' ... --- _id: '18276' 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." author: - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Stefan full_name: Werner, Stefan last_name: Werner - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: 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.' 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} }' 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.' 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.' mla: 'Tornede, Alexander, et al. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” ACML 2020, 2020.' short: 'A. Tornede, M.D. Wever, S. Werner, F. Mohr, E. Hüllermeier, in: ACML 2020, 2020.' conference: end_date: 2020-11-20 location: Bangkok, Thailand name: 12th Asian Conference on Machine Learning start_date: 2020-11-18 date_created: 2020-08-25T12:09:28Z date_updated: 2022-01-06T06:53:28Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng main_file_link: - url: https://arxiv.org/pdf/2007.02816.pdf project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: ACML 2020 status: public title: 'Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis' type: conference user_id: '5786' year: '2020' ... --- _id: '16725' author: - first_name: Cedric full_name: Richter, Cedric id: '50003' last_name: Richter - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Marie-Christine full_name: Jakobs, Marie-Christine last_name: Jakobs - first_name: Heike full_name: Wehrheim, Heike id: '573' last_name: Wehrheim citation: ama: Richter C, Hüllermeier E, Jakobs M-C, Wehrheim H. Algorithm Selection for Software Validation Based on Graph Kernels. Journal of Automated Software Engineering. 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. 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} }' 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. 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. mla: Richter, Cedric, et al. “Algorithm Selection for Software Validation Based on Graph Kernels.” Journal of Automated Software Engineering, Springer. short: C. Richter, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, Journal of Automated Software Engineering (n.d.). date_created: 2020-04-19T14:08:06Z date_updated: 2022-01-06T06:52:55Z department: - _id: '7' - _id: '77' - _id: '355' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '11' name: SFB 901 - Subproject B3 - _id: '12' name: SFB 901 - Subproject B4 publication: Journal of Automated Software Engineering publication_status: accepted publisher: Springer status: public title: Algorithm Selection for Software Validation Based on Graph Kernels type: journal_article user_id: '477' year: '2020' ... --- _id: '15629' 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. author: - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: 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.' 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.' 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} }' 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.' 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.' 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.' conference: end_date: 2020-04-27 location: Konstanz, Germany name: Symposium on Intelligent Data Analysis start_date: 2020-04-24 date_created: 2020-01-23T08:44:08Z date_updated: 2022-01-06T06:52:30Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication_status: accepted publisher: Springer status: public title: 'LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification' type: conference user_id: '5786' year: '2020' ... --- _id: '15025' abstract: - lang: eng 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. author: - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Lorijn full_name: van Rooijen, Lorijn id: '58843' last_name: van Rooijen - first_name: Heiko full_name: Hamann, Heiko last_name: Hamann citation: 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 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 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} }' 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.' 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.' 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. short: M.D. Wever, L. van Rooijen, H. Hamann, Evolutionary Computation 28 (2020) 165–193. date_created: 2019-11-18T14:19:19Z date_updated: 2022-01-06T06:52:15Z department: - _id: '34' - _id: '355' - _id: '26' - _id: '63' - _id: '238' doi: 10.1162/evco_a_00266 intvolume: ' 28' issue: '2' language: - iso: eng page: 165–193 project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '9' name: SFB 901 - Subproject B1 - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: Evolutionary Computation publication_status: published publisher: MIT Press Journals related_material: link: - relation: confirmation url: https://www.mitpressjournals.org/doi/pdf/10.1162/evco_a_00266 status: public title: Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets type: journal_article user_id: '15415' volume: 28 year: '2020' ... --- _id: '19523' abstract: - lang: eng 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." author: - first_name: Karlson full_name: Pfannschmidt, Karlson last_name: Pfannschmidt - first_name: Pritha full_name: Gupta, Pritha last_name: Gupta - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier citation: ama: 'Pfannschmidt K, Gupta P, Hüllermeier E. Learning Choice Functions: Concepts and Architectures. arXiv:190110860. 2019.' apa: 'Pfannschmidt, K., Gupta, P., & Hüllermeier, E. (2019). Learning Choice Functions: Concepts and Architectures. ArXiv:1901.10860.' 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} }' chicago: 'Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Learning Choice Functions: Concepts and Architectures.” ArXiv:1901.10860, 2019.' ieee: 'K. Pfannschmidt, P. Gupta, and E. Hüllermeier, “Learning Choice Functions: Concepts and Architectures,” arXiv:1901.10860. 2019.' mla: 'Pfannschmidt, Karlson, et al. “Learning Choice Functions: Concepts and Architectures.” ArXiv:1901.10860, 2019.' short: K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1901.10860 (2019). date_created: 2020-09-17T10:53:38Z date_updated: 2022-01-06T06:54:06Z department: - _id: '7' - _id: '355' language: - iso: eng project: - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: arXiv:1901.10860 status: public title: 'Learning Choice Functions: Concepts and Architectures' type: preprint user_id: '13472' year: '2019' ... --- _id: '17565' author: - first_name: Marie-Luis full_name: Merten, Marie-Luis last_name: Merten - first_name: Nina full_name: Seemann, Nina last_name: Seemann - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' citation: ama: Merten M-L, Seemann N, Wever MD. Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. Niederdeutsches Jahrbuch. 2019;(142):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. 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} }' 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. mla: Merten, Marie-Luis, et al. “Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff.” Niederdeutsches Jahrbuch, no. 142, 2019, pp. 124–46. short: M.-L. Merten, N. Seemann, M.D. Wever, Niederdeutsches Jahrbuch (2019) 124–146. date_created: 2020-08-03T13:55:04Z date_updated: 2022-01-06T06:53:15Z department: - _id: '34' - _id: '355' - _id: '26' issue: '142' language: - iso: ger page: 124-146 project: - _id: '39' name: InterGramm publication: Niederdeutsches Jahrbuch publication_status: published status: public title: Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff type: journal_article user_id: '5786' year: '2019' ...