AI RESEARCH
Proximal-Based Generative Modeling for Bayesian Inverse Problems
arXiv CS.LG
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ArXi:2605.13278v1 Announce Type: cross Score-based diffusion models nstrate superior performance in generative tasks but encounter fundamental bottlenecks in inverse problems due to the analytical intractability of the time-dependent likelihood score. To bridge this gap, we propose a novel proximal-based generative modeling (PGM) framework that rigorously circumvents explicit likelihood evaluation. Our framework is built upon a theoretical equivalence between Gaussian convolution in diffusion processes and Moreau-Yosida regularization in nonsmooth optimization.