Machine learning applications require fast and reliable per-sample uncertainty estimation. A common approach is to use predictive distributions from Bayesian or approximation methods and additively decompose uncertainty into aleatoric (i.e., data-related) and epistemic (i.e., model-related) components. However, additive decomposition has recently been questioned, with evidence that it breaks down when using finite-ensemble sampling and/or mismatched predictive distributions. This paper introduces Variance-Gated Ensembles (VGE), an intuitive, differentiable framework that injects epistemic sensitivity via a signal-to-noise gate computed from ensemble statistics. VGE provides: (i) a Variance-Gated Margin Uncertainty (VGMU) score that couples decision margins with ensemble predictive variance; and (ii) a Variance-Gated Normalization (VGN) layer that generalizes the variance-gated uncertainty mechanism to training via per-class, learnable normalization of ensemble member probabilities. We derive closed-form vector-Jacobian products enabling end-to-end training through ensemble sample mean and variance. VGE matches or exceeds state-of-the-art information-theoretic baselines while remaining computationally efficient. As a result, VGE provides a practical and scalable approach to epistemic-aware uncertainty estimation in ensemble models.

翻译：机器学习应用需要快速且可靠的逐样本不确定性估计。常用方法是通过贝叶斯或近似方法得到预测分布，并将不确定性加性分解为偶然（数据相关）和认知（模型相关）两部分。然而近期研究表明，加性分解在使用有限集采样或预测分布失配时可能失效。本文提出方差门控集成（VGE）——一种直观的可微框架，通过基于集成统计量计算的信号噪声门控注入认知敏感性。VGE提供：（1）方差门控边缘不确定性（VGMU）评分，将决策边界与集成预测方差耦合；（2）方差门控归一化（VGN）层，通过逐类可学习的集成成员概率归一化，将方差门控不确定性机制推广至训练过程。我们推导了闭式向量-雅可比积，使得通过集成样本均值和方差进行端到端训练成为可能。VGE在保持计算高效性的同时，匹配或超越了现有最优信息论基线方法，为集成模型中的认知感知不确定性估计提供了实用且可扩展的方案。