Training-Conditional Coverage Bounds for Uniformly Stable Learning Algorithms
<|reference_start|>Training-Conditional Coverage Bounds for Uniformly Stable Learning Algorithms: 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 bounds of full-conformal, jackknife+, and CV+ prediction regions from a uniform stability perspective which is known to hold for empirical risk minimization over reproducing kernel Hilbert spaces with convex regularization. We derive coverage bounds for finite-dimensional models by a concentration argument for the (estimated) predictor function, and compare the bounds with existing ones under ridge regression.<|reference_end|>
@article{pournaderi2024training-conditional,
    title={Training-Conditional Coverage Bounds for Uniformly Stable Learning
  Algorithms},
    author={Mehrdad Pournaderi and Yu Xiang},
    journal={arXiv preprint arXiv:2404.13731},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13731},
    primaryClass={stat.ML cs.LG}
}