Existing studies into ordinal optimization fall into one of two categories: fixed budget or fixed confidence. With fixed budget, the aim is to minimize the probability of false selection given a sampling budget. In a fixed confidence setting, the target is to come up with a sampling procedure that satisfies a desired guarantee on the probability of false
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