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arXiv · 2405.01761

Multivariate Bayesian Last Layer for Regression with Uncertainty Quantification and Decomposition

Han Wang·Eiji Kawasaki·Guillaume Damblin·Geoffrey Daniel
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Abstract

We present new Bayesian Last Layer neural network models in the setting of multivariate regression under heteroscedastic noise, and propose EM algorithms for parameter learning. Bayesian modeling of a neural network's final layer has the attractive property of uncertainty quantification with a single forward pass. The proposed framework is capable of disentangling the aleatoric and epistemic uncertainty, and can be used to enhance a canonically trained deep neural network with uncertainty-aware capabilities.

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Han Wang, Eiji Kawasaki, Guillaume Damblin, Geoffrey Daniel. 2026-01-30. Multivariate Bayesian Last Layer for Regression with Uncertainty Quantification and Decomposition. https://arxiv.org/abs/2405.01761

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