Brief

Researchers are developing new methods to tackle complex inverse problems in machine learning, particularly in scenarios where gradient information is unavailable. New techniques aim to improve sampling from high-dimensional, non-log-concave distributions by reducing variance and providing theoretical guarantees. These advancements are being applied to areas like image reconstruction and Bayesian inference, showing promise in enhancing accuracy and efficiency compared to existing methods. AI

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