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Near-Optimal Regret for Semi-Bandit Learning of Monotone Stochastic Problems
We consider the task of learning an (approximately) optimal policy for stochastic optimization problems where the true distributions are unknown. In each period, the algorithm plays a policy for the stochastic problem and observes semi-bandit feedback, i.e., it only receives new samples from the ran
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