Researchers have developed a novel stopping rule for Bayesian Deep Ensembles (BDEs) that utilizes E-values to determine when to cease MCMC sampling. This method addresses the practical challenge of balancing computational cost with uncertainty quantification improvements in deep learning models. By framing ensemble construction as a sequential hypothesis test, the approach efficiently identifies when further sampling offers diminishing returns over an optimized baseline, often requiring only a fraction of the full sampling budget. AI
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RANK_REASON The submission is an arXiv preprint detailing a new methodology for Bayesian Deep Ensembles.
