Localized conformal prediction: a generalized inference framework for conformal prediction
提出局部化共形预测框架，通过自适应构造测试样本的局部区域，在无假设下保证有限样本边际覆盖，并在适当假设下提供局部覆盖保证。
Summary We propose a new inference framework called localized conformal prediction. It generalizes the framework of conformal prediction by offering a single-test-sample adaptive construction that emphasizes a local region around this test sample, and can be combined with different conformal scores. The proposed framework enjoys an assumption-free finite sample marginal coverage guarantee, and it also offers additional local coverage guarantees under suitable assumptions. We demonstrate how to change from conformal prediction to localized conformal prediction using several conformal scores, and we illustrate a potential gain via numerical examples.