Sampling in High-Dimensions using Stochastic Interpolants and Forward-Backward Stochastic Differential Equations

• URL: http://arxiv.org/abs/2502.00355v1
• Date: Sat, 01 Feb 2025 07:27:11 GMT
• Title: Sampling in High-Dimensions using Stochastic Interpolants and Forward-Backward Stochastic Differential Equations
• Authors: Anand Jerry George, Nicolas Macris,
• Abstract summary: We present a class of diffusion-based algorithms to draw samples from high-dimensional probability distributions. Our approach relies on the interpolants framework to define a time-indexed collection of probability densities. We demonstrate that our algorithm can effectively draw samples from distributions that conventional methods struggle to handle.
• Score: 8.509310102094512
• License:
• Abstract: We present a class of diffusion-based algorithms to draw samples from high-dimensional probability distributions given their unnormalized densities. Ideally, our methods can transport samples from a Gaussian distribution to a specified target distribution in finite time. Our approach relies on the stochastic interpolants framework to define a time-indexed collection of probability densities that bridge a Gaussian distribution to the target distribution. Subsequently, we derive a diffusion process that obeys the aforementioned probability density at each time instant. Obtaining such a diffusion process involves solving certain Hamilton-Jacobi-Bellman PDEs. We solve these PDEs using the theory of forward-backward stochastic differential equations (FBSDE) together with machine learning-based methods. Through numerical experiments, we demonstrate that our algorithm can effectively draw samples from distributions that conventional methods struggle to handle.

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