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Exploiting Relevance for Online Decision-Making in High-Dimensions
As our learning model, we consider a structured contextual multi-armed bandit (CMAB) with high-dimensional arm (action) and context (data) sets, where the rewards depend only on a few relevant dimensions of the joint context-arm set, possibly in a non-linear way.
Multi-objective Contextual Multi-armed Bandit with a Dominant Objective
In this case, the optimal arm given a context is the one that maximizes the expected reward in the non-dominant objective among all arms that maximize the expected reward in the dominant objective.
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