Learning Orthogonal Projections in Linear Bandits

26 Jun 2019Qiyu KangWee Peng Tay

In a linear stochastic bandit model, each arm is a vector in an Euclidean space and the observed return at each time step is an unknown linear function of the chosen arm at that time step. In this paper, we investigate the problem of learning the best arm in a linear stochastic bandit model, where each arm's expected reward is an unknown linear function of the projection of the arm onto a subspace... (read more)

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