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.

翻译：共形预测是一种简单且强大的工具，无需任何分布假设即可量化不确定性。现有许多方法仅能保证平均覆盖，相较于更强的条件覆盖保证而言并不理想。现有近似条件覆盖的方法需要额外模型或时间成本，难以扩展。本文利用核密度估计对条件分布进行局部近似，提出一种修正的非一致性得分。该修正得分继承了拆分共形方法简单高效的特性，能够扩展到高维场景。我们还提出一个统一框架，将我们的方法与多种最先进方法整合。通过大量实证评估，基于平均覆盖和条件覆盖的测量结果均证实了我们方法的优势。