在线样本选择的阈值规则
Threshold rules for online sample selection
摘要：
我们考虑下面的样本选择问题。我们以在线方式观察一系列样本，每个样本都被赋予一种质量。我们的目标是选择或拒绝每个样本，以便最大限度地提高迄今为止所选子样本的总体质量。在选择率和子样本的总体质量之间存在自然的权衡。我们发现，对于一些这样的问题，非常简单和不经意的“阈值规则”的选择实现最佳的选择率和总质量之间的权衡在概率意义上。在某些情况下，我们表明，相同的阈值规则是最佳的一大类质量分布，因此在很强的意义上是不经意的。
英文摘要：
We consider the following sample selection problem. We observe in an online fashion a sequence of samples, each endowed by a quality. Our goal is to either select or reject each sample, so as to maximize the aggregate quality of the subsample selected so far. There is a natural trade-off here between the rate of selection and the aggregate quality of the subsample. We show that for a number of such problems extremely simple and oblivious "threshold rules" for selection achieve optimal tradeoffs between rate of selection and aggregate quality in a probabilistic sense. In some cases we show that the same threshold rule is optimal for a large class of quality distributions and is thus oblivious in a strong sense.
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