arXiv:2602.08142v2 Announce Type: replace Abstract: 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,
The increasing reliance on AI for critical applications necessitates more reliable uncertainty estimation, and current methods are proving insufficient, particularly in nuanced decomposition.
Reliable per-sample uncertainty estimation is crucial for deploying AI models in high-stakes environments, enabling safer and more trustworthy autonomous systems and critical decision support.
The proposed Variance-Gated Ensembles (VGE) offer an improved framework for distinguishing between different sources of uncertainty, potentially leading to more robust and explainable AI systems.
- · AI developers
- · High-stakes AI applications
- · Safety-critical industries
- · Approximation methods relying on additive decomposition
- · AI models with opaque uncertainty metrics
More sophisticated and trustworthy AI models become deployable in regulated sectors.
Increased adoption of AI in fields requiring high epistemic awareness, such as autonomous driving and medical diagnosis.
Reduced regulatory hurdles for AI deployment due to improved transparency and reliability of uncertainty quantification.
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Read at arXiv cs.LG