用于不确定性估计的矩多重校准
Moment Multicalibration for Uncertainty Estimation
摘要：
我们展示了如何实现Hébert-Johnson et al. [2018]中的“多重校准”概念，不仅适用于均值，还适用于方差和其他高阶矩。非正式地说，这意味着我们可以找到回归函数，给定一个数据点，不仅可以预测其标签的期望值，还可以预测其标签分布的更高时刻，并且这些预测不仅在总体上平均时匹配真实分布量，而且在大量精细定义的子组上平均时也是如此。它产生了一个原则性的方法来估计预测的不确定性在许多不同的子组，并诊断潜在的来源不公平的预测能力的功能，跨子组。作为一个应用程序，我们表明，我们的时刻估计可以用来获得边际预测区间，同时有效的平均在所有的（足够大的）子组的时刻multicalibration已获得。
英文摘要：
We show how to achieve the notion of "multicalibration" from Hébert-Johnson et al. [2018] not just for means, but also for variances and other higher moments. Informally, it means that we can find regression functions which, given a data point, can make point predictions not just for the expectation of its label, but for higher moments of its label distribution as well-and those predictions match the true distribution quantities when averaged not just over the population as a whole, but also when averaged over an enormous number of finely defined subgroups. It yields a principled way to estimate the uncertainty of predictions on many different subgroups-and to diagnose potential sources of unfairness in the predictive power of features across subgroups. As an application, we show that our moment estimates can be used to derive marginal prediction intervals that are simultaneously valid as averaged over all of the (sufficiently large) subgroups for which moment multicalibration has been obtained.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
