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Learn user preferences across thousands of items from just tens of node evaluations by exploiting graph smoothness in a new spectral bandit framework.
TrailBlazer offers a computationally efficient Monte-Carlo planning algorithm that drastically reduces sample complexity by focusing exploration on near-optimal state trajectories within an MDP.
