论文标题
分位数风险控制：一个灵活的框架，用于界定高损失预测的概率
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions
论文作者
论文摘要
为了确保其负责任的使用，必须严格保证预测算法的性能。先前的工作主要集中在界定预测因子的预期损失上，但这在错误分布非常重要的许多风险敏感应用中还不够。在这项工作中，我们提出了一个灵活的框架，以在预测因子产生的损失分布的分位数上产生一个界限。我们的方法利用了观察到的损失值的顺序统计数据，而不是单独依靠样本平均值。我们表明，分位数是量化预测性能的一种信息，我们的框架适用于各种基于分位数的指标，每个指标都针对数据分布的重要子集。我们分析了我们提出的方法的理论特性，并证明了其在几个现实世界数据集上严格控制损失分位数的能力。
Rigorous guarantees about the performance of predictive algorithms are necessary in order to ensure their responsible use. Previous work has largely focused on bounding the expected loss of a predictor, but this is not sufficient in many risk-sensitive applications where the distribution of errors is important. In this work, we propose a flexible framework to produce a family of bounds on quantiles of the loss distribution incurred by a predictor. Our method takes advantage of the order statistics of the observed loss values rather than relying on the sample mean alone. We show that a quantile is an informative way of quantifying predictive performance, and that our framework applies to a variety of quantile-based metrics, each targeting important subsets of the data distribution. We analyze the theoretical properties of our proposed method and demonstrate its ability to rigorously control loss quantiles on several real-world datasets.
