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Pure exploration and regret minimization in matching bandits

Sentenac, Flore, Yi, Jialin, Calauzènes, ‪Clément, Perchet, Vianney and Vojnovic, Milan (2021) Pure exploration and regret minimization in matching bandits. In: Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research,139. Journal of Machine Learning Research, pp. 9434-9442. (In Press)

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Finding an optimal matching in a weighted graph is a standard combinatorial problem. We consider its semi-bandit version where either a pair or a full matching is sampled sequentially. We prove that it is possible to leverage a rank-1 assumption on the adjacency matrix to reduce the sample complexity and the regret of off-the-shelf algorithms up to reaching a linear dependency in the number of vertices (up to poly log terms).

Item Type: Book Section
Official URL:
Additional Information: © 2022 The Author(s).
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 30 Sep 2022 09:39
Last Modified: 30 Sep 2022 15:00

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