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Collaborating Authors

 Roshan Shariff

Differentially Private Contextual Linear Bandits

Neural Information Processing Systems

Neural Information Processing Systems http://nips.cc/

Differentially Private Contextual Linear Bandits

Neural Information Processing Systems

We study the contextual linear bandit problem, a version of the standard stochastic multi-armed bandit (MAB) problem where a learner sequentially selects actions to maximize a reward which depends also on a user provided per-round context. Though the context is chosen arbitrarily or adversarially, the reward is assumed to be a stochastic function of a feature vector that encodes the context and selected action. Our goal is to devise private learners for the contextual linear bandit problem.