Decision makers may suffer from uncertainty induced by limited data. This may be mitigated by accounting for epistemic uncertainty, which is however challenging to estimate efficiently for large neural networks. To this extent we investigate Delta Variances, a family of algorithms for epistemic uncertainty quantification, that is computationally efficient and convenient to implement. It can be applied to neural networks and more general functions composed of neural networks. As an example we consider a weather simulator with a neural-network-based step function inside -- here Delta Variances empirically obtain competitive results at the cost of a single gradient computation. The approach is convenient as it requires no changes to the neural network architecture or training procedure. We discuss multiple ways to derive Delta Variances theoretically noting that special cases recover popular techniques and present a unified perspective on multiple related methods. Finally we observe that this general perspective gives rise to a natural extension and empirically show its benefit.

本研究解决了决策者因数据有限而面临的不确定性问题，提出了Delta方差这一高效计算的算法族用于定量化认知不确定性。研究表明，该方法在不改变神经网络架构或训练过程的情况下，对神经网络及其组合函数进行不确定性估计，能够有效提升模型的竞争力，并为相关方法提供了统一的理论视角。

通用不确定性估计与Delta方差