HEDGE generates hypergraphs via a linear-Gaussian forward diffusion on incidence matrices with a hypergraph-specific heat operator, then learns a permutation-equivariant reverse drift to sample from the Gaussian base.
Tuning-free maximum likelihood training of latent variable models via coin betting
2 Pith papers cite this work. Polarity classification is still indexing.
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Tempered Guided Diffusion uses annealed SMC to produce consistent particle approximations to the posterior for training-free conditional diffusion sampling, outperforming independent guided trajectories in experiments.
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Hypergraph Generation via Structured Stochastic Diffusion
HEDGE generates hypergraphs via a linear-Gaussian forward diffusion on incidence matrices with a hypergraph-specific heat operator, then learns a permutation-equivariant reverse drift to sample from the Gaussian base.
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Tempered Guided Diffusion
Tempered Guided Diffusion uses annealed SMC to produce consistent particle approximations to the posterior for training-free conditional diffusion sampling, outperforming independent guided trajectories in experiments.