Stochastic Localization via Iterative Posterior Sampling

• URL: http://arxiv.org/abs/2402.10758v2
• Date: Tue, 28 May 2024 12:05:08 GMT
• Title: Stochastic Localization via Iterative Posterior Sampling
• Authors: Louis Grenioux, Maxence Noble, Marylou Gabrié, Alain Oliviero Durmus,
• Abstract summary: We consider a general localization framework and introduce an explicit class of observation processes, associated with flexible denoising schedules. We provide a complete methodology, $textitStochastic localization via Iterative Posterior Sampling$ (SLIPS), to obtain approximate samples of this dynamics, and as a byproduct, samples from the target distribution. We illustrate the benefits and applicability of SLIPS on several benchmarks of multi-modal distributions, including mixtures in increasing dimensions, logistic regression and high-dimensional field system from statistical-mechanics.
• Score: 2.1383136715042417
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Building upon score-based learning, new interest in stochastic localization techniques has recently emerged. In these models, one seeks to noise a sample from the data distribution through a stochastic process, called observation process, and progressively learns a denoiser associated to this dynamics. Apart from specific applications, the use of stochastic localization for the problem of sampling from an unnormalized target density has not been explored extensively. This work contributes to fill this gap. We consider a general stochastic localization framework and introduce an explicit class of observation processes, associated with flexible denoising schedules. We provide a complete methodology, $\textit{Stochastic Localization via Iterative Posterior Sampling}$ (SLIPS), to obtain approximate samples of this dynamics, and as a by-product, samples from the target distribution. Our scheme is based on a Markov chain Monte Carlo estimation of the denoiser and comes with detailed practical guidelines. We illustrate the benefits and applicability of SLIPS on several benchmarks of multi-modal distributions, including Gaussian mixtures in increasing dimensions, Bayesian logistic regression and a high-dimensional field system from statistical-mechanics.

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