Uncertainty Quantification in Machine Learning has progressed to predicting the source of uncertainty in a prediction: Uncertainty from stochasticity in the data (aleatoric), or uncertainty from limitations of the model (epistemic). Generally, each uncertainty is evaluated in isolation, but this obscures the fact that they are often not truly disentangled. This work proposes a set of experiments to evaluate disentanglement of aleatoric and epistemic uncertainty, and uses these methods to compare two competing formulations for disentanglement (the Information Theoretic approach, and the Gaussian Logits approach). The results suggest that the Information Theoretic approach gives better disentanglement, but that either predicted source of uncertainty is still largely contaminated by the other for both methods. We conclude that with the current methods for disentangling, aleatoric and epistemic uncertainty are not reliably separated, and we provide a clear set of experimental criteria that good uncertainty disentanglement should follow.

该研究解决了机器学习中不确定性量化的一个重要问题，即如何有效区分来自数据随机性（内在不确定性）和模型局限性（外在不确定性）的不确定性。通过实验比较了信息论方法和高斯对数法两种分离不确定性的方法，结果表明信息论方法能更好地实现分离，但两种不确定性仍相互污染，提供了一套评估有效分离的不确定性标准。

你的分类不确定性有多分离？