arXiv AI
Flow Annealing Posterior Sampling for Function-Space Regression and Inverse Problems
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Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems
arXiv:2606. 26592v1 Announce Type: cross Abstract: We propose latent-space diffusion posterior sampling (L-DPS), an approximate Bayesian framework for high-dimensional inverse problems governed by partial differential equations (PDEs).
arXiv Machine Learning
Jun 2
Preconditioned One-Step Generative Modeling for Bayesian Inverse Problems in Function Spaces
arXiv:2603. 14798v2 Announce Type: replace-cross Abstract: We propose a machine-learning algorithm for Bayesian inverse problems in the function-space regime.
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FlowSGS: Improving Flow Matching Priors for Inverse Imaging with Stochastic Interpolants
FlowSGS introduces a flow-based posterior sampling method that combines Split Gibbs Sampling (SGS) with Langevin dynamics for the likelihood step and Stochastic Interpolants (SI) for the prior step. By integrating a pretrained flow model into the prior step via SI's reverse-time SDE and a novel timestep correction, FlowSGS reduces the number of network evaluations compared to plug‑and‑play diffusion samplers. Experiments demonstrate state‑of‑the‑art performance on various inverse problems, including the first flow‑based solution to a nonlinear inverse problem (Fourier phase retrieval).
arXiv AI
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DAPS++: Rethinking Diffusion Inverse Problems with Decoupled Posterior Annealing
arXiv:2511. 17038v4 Announce Type: replace Abstract: From a Bayesian perspective, score-based diffusion solves inverse problems through joint inference, embedding the likelihood with the prior to guide the sampling process.
arXiv Machine Learning
Sep 18
PosteriorBench: From Point Estimates to Posterior Matching in Evaluating Generative Inverse Solvers
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arXiv AI
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Scale-Consistent Posterior Dynamics for Diffusion Inverse Problems
arXiv:2608. 15144v1 Announce Type: cross Abstract: Posterior sampling with a pretrained diffusion prior is governed by a conditional score whose intermediate likelihood component is generally intractable.