PGPS trains a neural velocity field to transport particles along a log-weighted shrinkage density path, giving a Wasserstein error bound of O(delta) + O(sqrt(h)) and improved mode seeking in Bayesian inference.
However, the curse of dimensionality for the kernel-based methods leads to the particle collapse in SVGD (Ba et al., 2021), i.e., variance collapse
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Path-Guided Particle-based Sampling
PGPS trains a neural velocity field to transport particles along a log-weighted shrinkage density path, giving a Wasserstein error bound of O(delta) + O(sqrt(h)) and improved mode seeking in Bayesian inference.