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协变量偏移下基于加权共形p值的无模型选择性推断AI 解读

Summary This paper introduces novel weighted conformal $ p $-values and methods for model-free selective inference. The problem is as follows: given test units with covariates $ X $ and missing responses $ Y $, how do we select units for which the responses $ Y $ are larger than user-specified values while controlling the proportion of false positives? Can we achieve this without modelling assumptions on the data and without restriction on the model for predicting the…

具有条件覆盖保证的共形预测AI 解读

Abstract We consider the problem of constructing distribution-free prediction sets with finite-sample conditional guarantees. Prior work has shown that it is impossible to provide exact conditional coverage universally in finite samples. Thus, most popular methods only guarantee marginal coverage over the covariates or are restricted to a limited set of conditional targets, e.g. coverage over a finite set of prespecified subgroups. This paper bridges this gap by defining a…