Inferring from an imprecise Plackett–Luce model : application to label ranking
- Author
- Loïc Adam, Arthur Van Camp (UGent) , Sébastien Destercke and Benjamin Quost
- Organization
- Abstract
- Learning ranking models is a difficult task, in which data may be scarce and cautious predictions desirable. To address such issues, we explore the extension of the popular parametric probabilistic Plackett–Luce model, often used to model rankings, to the imprecise setting where estimated parameters are set-valued. In particular, we study how to achieve cautious or conservative inference with it, and illustrate their application on label ranking problems, a specific supervised learning task.
- Keywords
- Preference learning, Cautious inference, Poor data
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8675154
- MLA
- Adam, Loïc, et al. “Inferring from an Imprecise Plackett–Luce Model : Application to Label Ranking.” Scalable Uncertainty Management, 14th International Conference, SUM 2020, edited by Jesse Davis and Karim Tabia, vol. 12322, Springer, Cham, 2020, pp. 98–112, doi:10.1007/978-3-030-58449-8_7.
- APA
- Adam, L., Van Camp, A., Destercke, S., & Quost, B. (2020). Inferring from an imprecise Plackett–Luce model : application to label ranking. In J. Davis & K. Tabia (Eds.), Scalable Uncertainty Management, 14th International Conference, SUM 2020 (Vol. 12322, pp. 98–112). https://doi.org/10.1007/978-3-030-58449-8_7
- Chicago author-date
- Adam, Loïc, Arthur Van Camp, Sébastien Destercke, and Benjamin Quost. 2020. “Inferring from an Imprecise Plackett–Luce Model : Application to Label Ranking.” In Scalable Uncertainty Management, 14th International Conference, SUM 2020, edited by Jesse Davis and Karim Tabia, 12322:98–112. Springer, Cham. https://doi.org/10.1007/978-3-030-58449-8_7.
- Chicago author-date (all authors)
- Adam, Loïc, Arthur Van Camp, Sébastien Destercke, and Benjamin Quost. 2020. “Inferring from an Imprecise Plackett–Luce Model : Application to Label Ranking.” In Scalable Uncertainty Management, 14th International Conference, SUM 2020, ed by. Jesse Davis and Karim Tabia, 12322:98–112. Springer, Cham. doi:10.1007/978-3-030-58449-8_7.
- Vancouver
- 1.Adam L, Van Camp A, Destercke S, Quost B. Inferring from an imprecise Plackett–Luce model : application to label ranking. In: Davis J, Tabia K, editors. Scalable Uncertainty Management, 14th International Conference, SUM 2020. Springer, Cham; 2020. p. 98–112.
- IEEE
- [1]L. Adam, A. Van Camp, S. Destercke, and B. Quost, “Inferring from an imprecise Plackett–Luce model : application to label ranking,” in Scalable Uncertainty Management, 14th International Conference, SUM 2020, Bozen-Bolzano, Italy, 2020, vol. 12322, pp. 98–112.
@inproceedings{8675154,
abstract = {{Learning ranking models is a difficult task, in which data may be scarce and cautious predictions desirable. To address such issues, we explore the extension of the popular parametric probabilistic Plackett–Luce model, often used to model rankings, to the imprecise setting where estimated parameters are set-valued. In particular, we study how to achieve cautious or conservative inference with it, and illustrate their application on label ranking problems, a specific supervised learning task.}},
author = {{Adam, Loïc and Van Camp, Arthur and Destercke, Sébastien and Quost, Benjamin}},
booktitle = {{Scalable Uncertainty Management, 14th International Conference, SUM 2020}},
editor = {{Davis, Jesse and Tabia, Karim}},
isbn = {{9783030584498}},
issn = {{0302-9743}},
keywords = {{Preference learning,Cautious inference,Poor data}},
language = {{eng}},
location = {{Bozen-Bolzano, Italy}},
pages = {{98--112}},
publisher = {{Springer, Cham}},
title = {{Inferring from an imprecise Plackett–Luce model : application to label ranking}},
url = {{http://doi.org/10.1007/978-3-030-58449-8_7}},
volume = {{12322}},
year = {{2020}},
}
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