论文标题
不确定性估计的矩数
Moment Multicalibration for Uncertainty Estimation
论文作者
论文摘要
我们展示了如何实现Hébert-Johnson等人的“多核算”概念。 [2018]不仅用于手段，还为方差和其他更高的时刻。非正式地，这意味着我们可以找到回归函数，鉴于数据点，不仅可以为其标签的期望做出点预测，而且可以为其标签分布的较高矩进行较高的时刻，并且这些预测与整个人群的平均数量不仅仅是整个人口相匹配，而且在整个人群中的平均值时，还与大量定义的子组相比。它产生了一种原则性的方法来估计许多不同亚组预测的不确定性，并诊断跨亚组特征的预测能力的潜在不公平来源。作为应用程序，我们表明我们的力矩估计可用于得出边际预测间隔，这些间隔是在所有（足够大的）亚组中平均的同时有效的，为此时刻进行了多核。
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.
