A new paper explores the relationship between the Evidence Lower Bound (ELBO) and Occam's Razor in Bayesian model selection. The research demonstrates that ELBO-based hyperparameter learning can lead to overfitting, contrary to the principle of Occam's Razor which favors simpler models. Surprisingly, Bayesian model selection using the evidence itself sometimes prefers the overfit model, while the ELBO does not. The findings suggest that practitioners should be cautious about how reduced-rank assumptions, necessary for tractability in large models, can impact model selection. AI
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IMPACT Highlights potential pitfalls in model selection for large Bayesian models, impacting practitioners in the field.
RANK_REASON Academic paper on a theoretical aspect of Bayesian inference and model selection.