Introduces a path-space stochastic control formulation for diffusion posterior sampling with time reparameterization and trust-region optimization to achieve more accurate sampling and importance-weighted corrections.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Unifies a broad class of generative models as instances of parametric JKO schemes for f-divergences, establishes equivalences, extends the framework to IPMs and squared MMD, and analyzes parametric Wasserstein flows.
citing papers explorer
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A Stabilized Path-Space Approach to Diffusion-Based Posterior Sampling
Introduces a path-space stochastic control formulation for diffusion posterior sampling with time reparameterization and trust-region optimization to achieve more accurate sampling and importance-weighted corrections.
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A Unifying View of Variational Generative Wasserstein Flows
Unifies a broad class of generative models as instances of parametric JKO schemes for f-divergences, establishes equivalences, extends the framework to IPMs and squared MMD, and analyzes parametric Wasserstein flows.