Konsep Inti
The core message of this paper is to develop a method for forming valid confident prediction sets in the out-of-distribution (OOD) generalization setting, where the test distribution differs from the training distribution.
Abstrak
The paper studies the confidence set prediction problem in the OOD generalization setting. It first shows that the standard split conformal prediction (SCP) method fails to maintain the desired marginal coverage when the test distribution differs from the training distribution.
To address this issue, the paper proposes a new method for forming confident prediction sets in the OOD setting. The key idea is to construct the prediction set based on the f-divergence between the test distribution and the convex hull of the training distributions. The paper provides a theoretical analysis to show that the proposed method is guaranteed to maintain the marginal coverage for any target distribution within a certain f-divergence ball of the training distributions.
The paper also considers the practical case where only the empirical distributions of the training data are available. It provides a corrected version of the prediction set that still maintains the marginal coverage guarantee in this setting.
Finally, the paper conducts simulation experiments to verify the correctness of the theoretical results and the validity of the proposed method.