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Dimension-Uniform Discretization Analysis of Preconditioned Annealed Langevin Dynamics for Multimodal Gaussian Mixtures
Lorenzo Baldassari, Josselin Garnier, Knut Solna +1
Obtaining stable diffusion-based samplers in high- and infinite-dimensional settings is challenging because errors can accumulate across high-frequency coordinates and make the dyn…
Preconditioned Langevin Dynamics with Score-Based Generative Models for Infinite-Dimensional Linear Bayesian Inverse Problems
Lorenzo Baldassari, Josselin Garnier, Knut Solna +1
Designing algorithms for solving high-dimensional Bayesian inverse problems directly in infinite-dimensional function spaces - where such problems are naturally formulated - is cru…
Taming Score-Based Diffusion Priors for Infinite-Dimensional Nonlinear Inverse Problems
Lorenzo Baldassari, Ali Siahkoohi, Josselin Garnier +2
This work introduces a sampling method capable of solving Bayesian inverse problems in function space. It does not assume the log-concavity of the likelihood, meaning that it is co…
Conditional score-based diffusion models for Bayesian inference in infinite dimensions
Lorenzo Baldassari, Ali Siahkoohi, Josselin Garnier +2
Since their initial introduction, score-based diffusion models (SDMs) have been successfully applied to solve a variety of linear inverse problems in finite-dimensional vector spac…