Active context sampling algorithm for contextual linear bandits achieves instance-dependent guarantees improving over minimax rate by up to sqrt(d) and reduces samples needed in empirical tasks.
Near-optimal policy identifi- cation in active reinforcement learning
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Active Learning for Stochastic Contextual Linear Bandits
Active context sampling algorithm for contextual linear bandits achieves instance-dependent guarantees improving over minimax rate by up to sqrt(d) and reduces samples needed in empirical tasks.