提出VSPS方法，高效量化多目标回归不确定性。
Volume-Sorted Prediction Set: Efficient Conformal Prediction for Multi-Target Regression
- 基于条件归一化流与置信校准，构建非凸预测集。
- 在保证覆盖概率前提下，预测区域更小更有效。
- 适合高维复杂场景下的不确定性建模需求。
我们提出体积排序预测集（VSPS），一种用于多目标回归的不确定性量化新方法，结合条件归一化流与置信校准。该方法通过学习使响应条件分布呈已知形式的变换，利用雅可比行列式识别原始空间中的密集区域，从而构建灵活且非凸的预测区域，确保覆盖概率。实验表明，VSPS在保持稳健覆盖的同时，生成更小、更具信息量的预测区域，显著提升复杂高维场景下的不确定性建模能力。
原文摘要 · Abstract (English)
We introduce Volume-Sorted Prediction Set (VSPS), a novel method for uncertainty quantification in multi-target regression that uses conditional normalizing flows with conformal calibration. This approach constructs flexible, non-convex predictive regions with guaranteed coverage probabilities, overcoming limitations of traditional methods. By learning a transformation where the conditional distribution of responses follows a known form, VSPS identifies dense regions in the original space using the Jacobian determinant. This enables the creation of prediction regions that adapt to the true underlying distribution, focusing on areas of high probability density. Experimental results demonstrate that VSPS produces smaller, more informative prediction regions while maintaining robust coverage guarantees, enhancing uncertainty modeling in complex, high-dimensional settings.
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