Informativeness of Weighted Conformal Prediction

• URL: http://arxiv.org/abs/2405.06479v3
• Date: Sun, 20 Oct 2024 02:18:43 GMT
• Title: Informativeness of Weighted Conformal Prediction
• Authors: Mufang Ying, Wenge Guo, Koulik Khamaru, Ying Hung,
• Abstract summary: We propose two methods to enhance the informativeness of weighted conformal prediction. We establish theoretical guarantees for our proposed methods and demonstrate their efficacy through simulations.
• Score: 3.1717575440579617
• License:
• Abstract: Weighted conformal prediction (WCP), a recently proposed framework, provides uncertainty quantification with the flexibility to accommodate different covariate distributions between training and test data. However, it is pointed out in this paper that the effectiveness of WCP heavily relies on the overlap between covariate distributions; insufficient overlap can lead to uninformative prediction intervals. To enhance the informativeness of WCP, we propose two methods for scenarios involving multiple sources with varied covariate distributions. We establish theoretical guarantees for our proposed methods and demonstrate their efficacy through simulations.

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