Personalizing Many Decisions with High-Dimensional Covariates
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), pp. 11469-11480, 2019.
We consider the k-armed stochastic contextual bandit problem with d dimensional features, when both k and d can be large. To the best of our knowledge, all existing algorithms for this problem have regret bounds that scale as polynomials of degree at least two, in k and d. The main contribution of this paper is to introduce and theoretica...More
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