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Nonparametric Contextual Bandits in Metric Spaces with Unknown Metric
lib:b67a7965da2d6a32 (v1.0.0)
Authors: Nirandika Wanigasekara,Christina Yu
Where published: NeurIPS 2019 12
Document: PDF DOI
Abstract URL: http://papers.nips.cc/paper/9609-nonparametric-contextual-bandits-in-metric-spaces-with-unknown-metric
Consider a nonparametric contextual multi-arm bandit problem where each arm $a \in [K]$ is associated to a nonparametric reward function $f_a: [0,1] \to \mathbb{R}$ mapping from contexts to the expected reward. Suppose that there is a large set of arms, yet there is a simple but unknown structure amongst the arm reward functions, e.g. finite types or smooth with respect to an unknown metric space. We present a novel algorithm which learns data-driven similarities amongst the arms, in order to implement adaptive partitioning of the context-arm space for more efficient learning. We provide regret bounds along with simulations that highlight the algorithm's dependence on the local geometry of the reward functions.
Where published: NeurIPS 2019 12
Document: PDF DOI
Abstract URL: http://papers.nips.cc/paper/9609-nonparametric-contextual-bandits-in-metric-spaces-with-unknown-metric
Consider a nonparametric contextual multi-arm bandit problem where each arm $a \in [K]$ is associated to a nonparametric reward function $f_a: [0,1] \to \mathbb{R}$ mapping from contexts to the expected reward. Suppose that there is a large set of arms, yet there is a simple but unknown structure amongst the arm reward functions, e.g. finite types or smooth with respect to an unknown metric space. We present a novel algorithm which learns data-driven similarities amongst the arms, in order to implement adaptive partitioning of the context-arm space for more efficient learning. We provide regret bounds along with simulations that highlight the algorithm's dependence on the local geometry of the reward functions.
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