Exploiting Concavity Information in Gaussian Process Contextual Bandit Optimization
The research introduces a contextual bandit algorithm that improves optimization by incorporating concavity knowledge into a Bayesian Gaussian Process model. By conditioning the posterior on concavity constraints, the method proposes a novel reward model that yields tighter regret bounds than standard methods. This is valuable in fields like precision medicine where action intensity has diminishing returns.