ORCID: https://orcid.org/0000-0002-9944-4108
(July 2020):
Preselection Bandits.
37th International Conference on Machine Learning, Virtual, July 12-18 2020.
III, Hal Daumé und Singh, Aarti (eds.) :
In: Proceedings of the 37th International Conference on Machine Learning,
Vol. 119
PMLR. pp. 778-787
[PDF, 630kB]


Abstract
In this paper, we introduce the Preselection Bandit problem, in which the learner preselects a subset of arms (choice alternatives) for a user, which then chooses the final arm from this subset. The learner is not aware of the user’s preferences, but can learn them from observed choices. In our concrete setting, we allow these choices to be stochastic and model the user’s actions by means of the Plackett-Luce model. The learner’s main task is to preselect subsets that eventually lead to highly preferred choices. To formalize this goal, we introduce a reasonable notion of regret and derive lower bounds on the expected regret. Moreover, we propose algorithms for which the upper bound on expected regret matches the lower bound up to a logarithmic term of the time horizon.
Item Type: | Conference or Workshop Item (Paper) |
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Form of publication: | Publisher's Version |
Faculties: | Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning |
Subjects: | 000 Computer science, information and general works > 000 Computer science, knowledge, and systems |
URN: | urn:nbn:de:bvb:19-epub-93176-6 |
Item ID: | 93176 |
Date Deposited: | 09. Sep 2022 11:04 |
Last Modified: | 17. Dec 2024 10:10 |