AI RESEARCH
Towards E-Value Based Stopping Rules for Bayesian Deep Ensembles
arXiv CS.LG
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ArXi:2604.18089v1 Announce Type: new Bayesian Deep Ensembles (BDEs) represent a powerful approach for uncertainty quantification in deep learning, combining the robustness of Deep Ensembles (DEs) with flexible multi-chain MCMC. While DEs are affordable in most deep learning settings, (long) sampling of Bayesian neural networks can be prohibitively costly. Yet, adding sampling after optimizing the DEs has been shown to yield significant improvements.