Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification

arXiv:2605.12208v2 Announce Type: replace-cross Abstract: Approximate Bayesian inference typically revolves around computing the posterior parameter distribution. In practice, however, the main object of interest is often a model's predictions rather than its parameters. In this work, we propose to bypass the parameter posterior and focus directly on approximating the posterior predictive distribution. We achieve this by drawing inspiration from self-training within self-supervised and semi-supervised learning. Essentially, we quantify a Bayesian model's predictive uncertainty by refitting on

Source: arXiv cs.LG — read the full report at the original publisher.

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