E-value sequential tests enable early stopping of MCMC sampling in Bayesian deep ensembles, often needing only a fraction of the full budget while improving over standard deep ensembles.
arXiv:2412.08876 , year=
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Sampling-based inference for Bayesian neural networks has achieved computational parity with optimization-based methods and should be prioritized to deliver better uncertainty quantification and model insights.
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Towards E-Value Based Stopping Rules for Bayesian Deep Ensembles
E-value sequential tests enable early stopping of MCMC sampling in Bayesian deep ensembles, often needing only a fraction of the full budget while improving over standard deep ensembles.
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Position: The Time for Sampling Is Now! Charting a New Course for Bayesian Deep Learning
Sampling-based inference for Bayesian neural networks has achieved computational parity with optimization-based methods and should be prioritized to deliver better uncertainty quantification and model insights.