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.
Symposium on Advances in Approximate Bayesian Inference , pages=
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bde is a new Python package that implements Bayesian deep ensembles via efficient JAX-based Microcanonical Langevin Ensembles for tabular regression and classification with uncertainty estimates.
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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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bde: A Python Package for Bayesian Deep Ensembles via MILE
bde is a new Python package that implements Bayesian deep ensembles via efficient JAX-based Microcanonical Langevin Ensembles for tabular regression and classification with uncertainty estimates.