提出一套量化概率模型不确定性的系统方法，提升预测可靠性判断能力。
Uncertainty Quantification in Probabilistic Machine Learning Models: Theory, Methods, and Insights
- 基于随机傅里叶特征的高斯过程，高效近似预测分布
- 分离并量化了认知不确定性与偶然不确定性，理论与实验结合
- 适合关注模型可信度评估的研究者与工程应用
不确定性量化（UQ）在概率机器学习模型中至关重要，尤其用于评估预测的可靠性。本文提出一种系统框架，用于估计概率模型中的认知不确定性和偶然不确定性。重点研究高斯过程潜变量模型，采用基于随机傅里叶特征的高斯过程实现预测分布的高效近似。推导了UQ的理论公式，提出基于蒙特卡洛采样的估计方法，并通过实验评估不确定性估计的影响。结果揭示了预测不确定性的来源，验证了所提方法在量化预测置信度方面的有效性。
原文摘要 · Abstract (English)
Uncertainty Quantification (UQ) is essential in probabilistic machine learning models, particularly for assessing the reliability of predictions. In this paper, we present a systematic framework for estimating both epistemic and aleatoric uncertainty in probabilistic models. We focus on Gaussian Process Latent Variable Models and employ scalable Random Fourier Features-based Gaussian Processes to approximate predictive distributions efficiently. We derive a theoretical formulation for UQ, propose a Monte Carlo sampling-based estimation method, and conduct experiments to evaluate the impact of uncertainty estimation. Our results provide insights into the sources of predictive uncertainty and illustrate the effectiveness of our approach in quantifying the confidence in the predictions.
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