Corrupt Bandits for Preserving Local Privacy

16 Aug 2017Pratik GajaneTanguy UrvoyEmilie Kaufmann

We study a variant of the stochastic multi-armed bandit (MAB) problem in which the rewards are corrupted. In this framework, motivated by privacy preservation in online recommender systems, the goal is to maximize the sum of the (unobserved) rewards, based on the observation of transformation of these rewards through a stochastic corruption process with known parameters... (read more)

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