Pure Exploration and Regret Minimization in Matching Bandits

• URL: http://arxiv.org/abs/2108.00230v1
• Date: Sat, 31 Jul 2021 12:37:51 GMT
• Title: Pure Exploration and Regret Minimization in Matching Bandits
• Authors: Flore Sentenac, Jialin Yi, Cl\'ement Calauz\`enes, Vianney Perchet, Milan Vojnovic
• Abstract summary: We prove that it is possible to leverage a rank-1 assumption on the adjacency matrix to reduce the sample complexity. Finding an optimal matching in a weighted graph is a standard problem.
• Score: 19.205538784019936
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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).

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