可解释的多变量符合预测：快速传递性标准化
Interpretable Multivariate Conformal Prediction with Fast Transductive Standardization
AI总结：
本文提出一种可解释的多变量符合预测方法，通过快速传递性标准化技术，实现紧致预测区间并保持有效覆盖。
摘要：
我们提出了一种符合预测方法，用于构建多个潜在相关数值输出的紧密同时预测区间，给定单个输入。该方法可以与任何多目标回归模型结合，并保证有限样本覆盖。它计算高效，即使在数据有限时也能产生信息丰富的预测区间。核心思想是一种新的坐标-wise 标准化程序，使不同输出维度的残差直接可比，利用校准数据本身估计合适的缩放参数。这不需要建模跨输出依赖性或辅助样本分割。实现这一想法需要克服与传递性或完整符合预测相关的技术挑战。在模拟和真实数据上的实验表明，该方法可以产生比现有基线更紧的预测区间，同时保持有效的同时覆盖。
英文摘要：
We propose a conformal prediction method for constructing tight simultaneous prediction intervals for multiple, potentially related, numerical outputs given a single input. This method can be combined with any multi-target regression model and guarantees finite-sample coverage. It is computationally efficient and yields informative prediction intervals even with limited data. The core idea is a novel \emph{coordinate-wise} standardization procedure that makes residuals across output dimensions directly comparable, estimating suitable scaling parameters using the calibration data themselves. This does not require modeling of cross-output dependence nor auxiliary sample splitting. Implementing this idea requires overcoming technical challenges associated with transductive or full conformal prediction. Experiments on simulated and real data demonstrate this method can produce tighter prediction intervals than existing baselines while maintaining valid simultaneous coverage.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
