提升回归模型在置信预测中的条件覆盖率，让高风险决策更可靠。
Adjusting Regression Models for Conditional Uncertainty Calibration
- 训练回归模型以优化分片置信预测的条件覆盖效果
- 建立条件覆盖率与名义率之间偏差的上界
- 适用于需要精准不确定性估计的医疗、金融等高风险场景
置信预测方法具有有限样本下无分布的边际覆盖保证，但通常无法提供条件覆盖保证，这对高风险决策尤为重要。本文提出一种新算法，通过训练回归函数来改进应用分片置信预测后的条件覆盖性能。我们建立了条件覆盖与名义覆盖率之间偏离的上界，并提出了端到端算法以控制该上界。我们在合成数据和真实世界数据集上实证验证了该方法的有效性。
原文摘要 · Abstract (English)
Conformal Prediction methods have finite-sample distribution-free marginal coverage guarantees. However, they generally do not offer conditional coverage guarantees, which can be important for high-stakes decisions. In this paper, we propose a novel algorithm to train a regression function to improve the conditional coverage after applying the split conformal prediction procedure. We establish an upper bound for the miscoverage gap between the conditional coverage and the nominal coverage rate and propose an end-to-end algorithm to control this upper bound. We demonstrate the efficacy of our method empirically on synthetic and real-world datasets.
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