提出新方法缓解分布偏移下预测集覆盖率下降问题
Coverage Guarantees for Pseudo-Calibrated Conformal Prediction under Distribution Shift
- 用伪校准结合源域信息调整置信阈值
- 理论证明覆盖率达标率受分类器误差和分布偏移量影响
- 通过不确定性动态调节标签生成，适合可靠预测场景
共形预测（CP）在可交换性假设下提供无分布保证的边际覆盖率，但在数据分布偏移时可能失效。本文分析了伪校准在有界标签条件协变量偏移模型下的应用，利用领域自适应工具，推导出目标覆盖率的下界，该下界依赖于分类器在源域的损失以及分布偏移的Wasserstein度量。基于此结果，提出一种设计伪校准集合的方法：通过松弛参数扩大共形阈值，确保目标覆盖率不低于预设水平。最后，提出一种源域调优的伪校准算法，根据分类器不确定性在硬伪标签与随机标签间插值。数值实验表明，理论边界能定性捕捉伪校准行为，且该方案在分布偏移下有效缓解覆盖率下降，同时保持非平凡的预测集大小。
原文摘要 · Abstract (English)
Conformal prediction (CP) offers distribution-free marginal coverage guarantees under an exchangeability assumption, but these guarantees can fail if the data distribution shifts. We analyze the use of pseudo-calibration as a tool to counter this performance loss under a bounded label-conditional covariate shift model. Using tools from domain adaptation, we derive a lower bound on target coverage in terms of the source-domain loss of the classifier and a Wasserstein measure of the shift. Using this result, we provide a method to design pseudo-calibrated sets that inflate the conformal threshold by a slack parameter to keep target coverage above a prescribed level. Finally, we propose a source-tuned pseudo-calibration algorithm that interpolates between hard pseudo-labels and randomized labels as a function of classifier uncertainty. Numerical experiments show that our bounds qualitatively track pseudo-calibration behavior and that the source-tuned scheme mitigates coverage degradation under distribution shift while maintaining nontrivial prediction set sizes.
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