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.
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design.arXiv preprint arXiv:2312.09236
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Error in approximating the tangent conditional score by the unconditional score in diffusion models is bounded by dimension-free conditional mutual information, with a projected-Langevin method outperforming baselines in inpainting and super-resolution.
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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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Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees
Error in approximating the tangent conditional score by the unconditional score in diffusion models is bounded by dimension-free conditional mutual information, with a projected-Langevin method outperforming baselines in inpainting and super-resolution.