用一个模型同时量化数据不确定性和认知不确定性，提升高风险场景下的预测可靠性。
HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Model
- 融合条件掩码自回归流与概率预测器，统一建模两类不确定性。
- 在深度估计等任务中，不确定性校准度优于现有方法，与实际误差更吻合。
- 可适配任意概率模型，适合需可信预测的医疗、自动驾驶等场景。
不确定性量化对高风险机器学习应用的鲁棒性至关重要。本文提出HybridFlow，一种模块化混合架构，通过结合条件掩码自回归流（用于估计偶然不确定性）与灵活的概率预测器（用于建模认知不确定性），统一建模两类不确定性。该框架可与任意概率模型类集成，用户无需牺牲预测性能即可轻松适配现有架构。在深度估计、多个回归基准以及冰盖模拟的科学案例研究中，HybridFlow均优于以往不确定性量化方法。实证结果表明，其量化出的不确定性具有良好的校准性，且与模型误差更一致。HybridFlow解决了贝叶斯深度学习中的关键挑战，以单一稳健框架实现两类不确定性的统一建模。
原文摘要 · Abstract (English)
Uncertainty quantification is critical for ensuring robustness in high-stakes machine learning applications. We introduce HybridFlow, a modular hybrid architecture that unifies the modeling of aleatoric and epistemic uncertainty by combining a Conditional Masked Autoregressive normalizing flow for estimating aleatoric uncertainty with a flexible probabilistic predictor for epistemic uncertainty. The framework supports integration with any probabilistic model class, allowing users to easily adapt HybridFlow to existing architectures without sacrificing predictive performance. HybridFlow improves upon previous uncertainty quantification frameworks across a range of regression tasks, such as depth estimation, a collection of regression benchmarks, and a scientific case study of ice sheet emulation. We also provide empirical results of the quantified uncertainty, showing that the uncertainty quantified by HybridFlow is calibrated and better aligns with model error than existing methods for quantifying aleatoric and epistemic uncertainty. HybridFlow addresses a key challenge in Bayesian deep learning, unifying aleatoric and epistemic uncertainty modeling in a single robust framework.
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