Uncertainty Estimation using Variance-Gated Distributions

• URL: http://arxiv.org/abs/2509.08846v1
• Date: Sun, 07 Sep 2025 16:19:21 GMT
• Title: Uncertainty Estimation using Variance-Gated Distributions
• Abstract summary: We propose an intuitive framework for uncertainty estimation and decomposition based on the signal-to-noise ratio of class probability distributions.<n>We introduce a variance-gated measure that scales predictions by a confidence factor derived from ensembles.
• Score: 0.6340400318304492
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Evaluation of per-sample uncertainty quantification from neural networks is essential for decision-making involving high-risk applications. A common approach is to use the predictive distribution from Bayesian or approximation models and decompose the corresponding predictive uncertainty into epistemic (model-related) and aleatoric (data-related) components. However, additive decomposition has recently been questioned. In this work, we propose an intuitive framework for uncertainty estimation and decomposition based on the signal-to-noise ratio of class probability distributions across different model predictions. We introduce a variance-gated measure that scales predictions by a confidence factor derived from ensembles. We use this measure to discuss the existence of a collapse in the diversity of committee machines.

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