论文标题
分裂局部保形预测
Split Localized Conformal Prediction
论文作者
论文摘要
共形预测是一种简单而强大的工具，可以量化不确定性而无需任何分布假设。许多现有方法仅解决平均覆盖范围保证，这与更强的条件覆盖范围保证相比并不理想。现有的近似条件覆盖范围的方法需要额外的模型或时间努力，这使得它们不容易扩展。在本文中，我们通过使用内核密度估计来利用条件分布的局部近似，提出了修改的不符合得分。修改后的分数继承了分裂保形方法的精神，该方法简单有效，可以扩展到高维设置。我们还提出了一个统一的框架，将我们的方法和几种最新方法汇集在一起。我们进行广泛的经验评估：通过平均和条件覆盖范围衡量的结果证实了我们方法的优势。
Conformal prediction is a simple and powerful tool that can quantify uncertainty without any distributional assumptions. Many existing methods only address the average coverage guarantee, which is not ideal compared to the stronger conditional coverage guarantee. Existing methods of approximating conditional coverage require additional models or time effort, which makes them not easy to scale. In this paper, we propose a modified non-conformity score by leveraging the local approximation of the conditional distribution using kernel density estimation. The modified score inherits the spirit of split conformal methods, which is simple and efficient and can scale to high dimensional settings. We also proposed a unified framework that brings together our method and several state-of-the-art. We perform extensive empirical evaluations: results measured by both average and conditional coverage confirm the advantage of our method.
