Pure Exploration and Regret Minimization in Matching Bandits
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:9434-9442, 2021.
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 to poly-log terms).