Contextual Decision-making under Parametric Uncertainty and Data-driven Optimistic Optimization
We consider decision-making problems with contextual information, in which the reward function involves uncertain parameters that can be predicted using covariates. To quantify the uncertainty of the reward, we propose a new parameter uncertainty set based on a supervised learning oracle. We show that the worst/best-case reward over the proposed parameter uncertainty set serves as … Read more