Establishes a fundamental weak convergence theorem for nonsingular SVIEs and derives first-order weak rates for the stochastic theta method and Wong-Zakai approximation while relaxing boundedness assumptions on the diffusion coefficient.
Weak Error Analysis for Strong Approximation Schemes of
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A consistency-regularized Euclidean-Wasserstein-2 gradient flow performs joint posterior sampling and prompt optimization in latent space for efficient low-NFE inverse problem solving with diffusion models.
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Fundamental weak convergence theorem for stochastic Volterra integral equations and its applications
Establishes a fundamental weak convergence theorem for nonsingular SVIEs and derives first-order weak rates for the stochastic theta method and Wong-Zakai approximation while relaxing boundedness assumptions on the diffusion coefficient.
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Consistency Regularised Gradient Flows for Inverse Problems
A consistency-regularized Euclidean-Wasserstein-2 gradient flow performs joint posterior sampling and prompt optimization in latent space for efficient low-NFE inverse problem solving with diffusion models.