通过集成分位数回归实现不确定性的分离，提升科学与工程建模的可靠性。
Uncertainty separation via ensemble quantile regression
- 用多个分位数回归模型构建集成框架，分离随机与认知不确定性。
- 在合成数据集上优于深度集成和蒙特卡洛丢弃法，尤其提升随机不确定性估计精度。
- 适合需要高可信度不确定性分析的科研与工程数据建模场景。
本文提出一种新颖且可扩展的不确定性估计与分离框架，适用于科学与工程中依赖数据建模的任务，其中可靠的不确定性量化至关重要。通过集成分位数回归（E-QR）模型，该方法在保持认知不确定性质量的同时，提升了随机不确定性估计性能，优于深度集成（DE）和蒙特卡洛（MC）丢弃等现有方法。为解决不确定性类型分离的挑战，我们提出一种迭代优化算法，通过在高不确定性区域进行渐进采样逐步改进分离效果。该框架可扩展至大规模数据集，在合成基准测试中表现优异，为数据驱动应用提供了稳健的不确定性量化工具。
原文摘要 · Abstract (English)
This paper introduces a novel and scalable framework for uncertainty estimation and separation with applications in data driven modeling in science and engineering tasks where reliable uncertainty quantification is critical. Leveraging an ensemble of quantile regression (E-QR) models, our approach enhances aleatoric uncertainty estimation while preserving the quality of epistemic uncertainty, surpassing competing methods, such as Deep Ensembles (DE) and Monte Carlo (MC) dropout. To address challenges in separating uncertainty types, we propose an algorithm that iteratively improves separation through progressive sampling in regions of high uncertainty. Our framework is scalable to large datasets and demonstrates superior performance on synthetic benchmarks, offering a robust tool for uncertainty quantification in data-driven applications.
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