Training-Conditional Coverage Bounds for Uniformly Stable Learning Algorithms

arXiv:2404.13731v1 Announce Type: cross
Abstract: The training-conditional coverage performance of the conformal prediction is known to be empirically sound. Recently, there have been efforts to support this observation with theoretical guarantees. The training-conditional coverage bounds for jackknife+ and full-conformal prediction regions have been established via the notion of $(m,n)$-stability by Liang and Barber~[2023]. Although this notion is weaker than uniform stability, it is not clear how to evaluate it for practical models. In this paper, we study the training-conditional coverage …

abstract algorithms arxiv coverage cs.lg notion observation performance prediction sound stability stat.ml support training type via