Efficient Contextual Bandits with Knapsacks via Regression

We consider contextual bandits with knapsacks (CBwK), a variant of the contextual bandit which places global constraints on budget consumption. We present a new algorithm that is simple, statistically optimal, and computationally efficient. Our algorithm combines LagrangeBwK (Immorlica et al., FOCS'19), a Lagrangian-based technique for CBwK, and SquareCB (Foster and Rakhlin, ICML'20), a regression-based technique for contextual bandits. Our analysis emphasizes the modularity of both techniques.