The prototype selection problem aims to learn a sparse distribution of a source set such that it best matches a different target set. The applications of this problem include target subset selection, data summarization, and clustering, to name a few. In this talk, we present efficient algorithms for the prototype selection problem. Using the optimal transport theory, the proposed optimization formulation is an instance of submodular maximization, and therefore, we propose a greedy algorithm with simple updates. We further make use of the bandit setup to reduce the computations. The presentation is based on the papers [1, 2].