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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.08846 |
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| _version_ | 1866908531482951680 |
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| author | Gillis, H. Martin Xu, Isaac Trappenberg, Thomas |
| author_facet | Gillis, H. Martin Xu, Isaac Trappenberg, Thomas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08846 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Uncertainty Estimation using Variance-Gated Distributions Gillis, H. Martin Xu, Isaac Trappenberg, Thomas Machine Learning Artificial Intelligence 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. |
| title | Uncertainty Estimation using Variance-Gated Distributions |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.08846 |