论文标题
带有控制变体的样品有效间隔估计的框架
A Framework for Sample Efficient Interval Estimation with Control Variates
论文作者
论文摘要
我们考虑对随机变量的平均值估算置信区间的问题，该目标的目标是为给定数量的样本产生最小的可能间隔。在一般情况下，最小值最佳算法以此问题而闻名，但在其他假设下，可以提高性能。特别是，我们设计了一种估计算法来利用控制变量的形式，利用顺序统计。在对控制变化质量的某些条件下，与现有估计算法相比，我们显示出提高的渐近效率。从经验上讲，我们在几个现实世界的测量和估计任务上展示了卓越的性能，在这些任务和估计任务中，我们将回归模型的输出作为控制变体。
We consider the problem of estimating confidence intervals for the mean of a random variable, where the goal is to produce the smallest possible interval for a given number of samples. While minimax optimal algorithms are known for this problem in the general case, improved performance is possible under additional assumptions. In particular, we design an estimation algorithm to take advantage of side information in the form of a control variate, leveraging order statistics. Under certain conditions on the quality of the control variates, we show improved asymptotic efficiency compared to existing estimation algorithms. Empirically, we demonstrate superior performance on several real world surveying and estimation tasks where we use the output of regression models as the control variates.
