Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification
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 self-predicted data. The idea is strikingly simple: If a model assigns high likelihood to self-predicted data, these predictions are of low uncertainty, an...
| Authors | Julian Rodemann, Alexander Marquard, Thomas Augustin et al. |
|---|---|
| Primary category | stat.ML |
| Published | 12 May 2026 |
| Last updated at source | 28 May 2026 |
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