Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification 事件

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