WQLCP: Weighted Adaptive Conformal Prediction for Robust Uncertainty Quantification Under Distribution Shifts

• URL: http://arxiv.org/abs/2505.19587v1
• Date: Mon, 26 May 2025 07:00:15 GMT
• Title: WQLCP: Weighted Adaptive Conformal Prediction for Robust Uncertainty Quantification Under Distribution Shifts
• Authors: Shadi Alijani, Homayoun Najjaran,
• Abstract summary: We introduce reconstruction losses derived from a Variational Autoencoder (VAE) as an uncertainty metric to scale score functions.<n>We propose weighted Quantile Loss-scaled Conformal Prediction (WQLCP) which refines RL SCP by incorporating a weighted notion of exchangeability.
• Score: 4.192712667327956
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conformal prediction (CP) provides a framework for constructing prediction sets with guaranteed coverage, assuming exchangeable data. However, real-world scenarios often involve distribution shifts that violate exchangeability, leading to unreliable coverage and inflated prediction sets. To address this challenge, we first introduce Reconstruction Loss-Scaled Conformal Prediction (RLSCP), which utilizes reconstruction losses derived from a Variational Autoencoder (VAE) as an uncertainty metric to scale score functions. While RLSCP demonstrates performance improvements, mainly resulting in better coverage, it quantifies quantiles based on a fixed calibration dataset without considering the discrepancies between test and train datasets in an unexchangeable setting. In the next step, we propose Weighted Quantile Loss-scaled Conformal Prediction (WQLCP), which refines RLSCP by incorporating a weighted notion of exchangeability, adjusting the calibration quantile threshold based on weights with respect to the ratio of calibration and test loss values. This approach improves the CP-generated prediction set outputs in the presence of distribution shifts. Experiments on large-scale datasets, including ImageNet variants, demonstrate that WQLCP outperforms existing baselines by consistently maintaining coverage while reducing prediction set sizes, providing a robust solution for CP under distribution shifts.

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