Low Rank Based Subspace Inference for the Laplace Approximation of Bayesian Neural Networks

ArXi:2502.02345v2 Announce Type: replace Subspace inference for neural networks assumes that a subspace of their parameter space suffices to produce a reliable uncertainty quantification. In this work, we underpin the validity of this assumption by using low rank techniques. We derive an expression for a subspace model to a Bayesian inference scenario based on the Laplace approximation that is, in a certain sense, optimal given a specific dataset.