Malliavin-Bismut Score-based Diffusion Models

• URL: http://arxiv.org/abs/2503.16917v1
• Date: Fri, 21 Mar 2025 07:27:10 GMT
• Title: Malliavin-Bismut Score-based Diffusion Models
• Authors: Ehsan Mirafzali, Utkarsh Gupta, Patrick Wyrod, Frank Proske, Daniele Venturi, Razvan Marinescu,
• Abstract summary: We introduce a new framework that employs Malliavin calculus to derive explicit expressions for the score function.<n>In doing so, we establish a rigorous connection between the Malliavin derivative, its adjoint, Bismut's formula, and diffusion generative models.<n>We derive a closed-form expression for $nabla log p_t(x)$ for nonlinear SDEs with state-independent diffusion coefficients.
• Score: 4.8357445794151594
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a new framework that employs Malliavin calculus to derive explicit expressions for the score function -- i.e., the gradient of the log-density -- associated with solutions to stochastic differential equations (SDEs). Our approach integrates classical integration-by-parts techniques with modern tools, such as Bismut's formula and Malliavin calculus, to address linear and nonlinear SDEs. In doing so, we establish a rigorous connection between the Malliavin derivative, its adjoint (the Malliavin divergence or the Skorokhod integral), Bismut's formula, and diffusion generative models, thus providing a systematic method for computing $\nabla \log p_t(x)$. For the linear case, we present a detailed study proving that our formula is equivalent to the actual score function derived from the solution of the Fokker--Planck equation for linear SDEs. Additionally, we derive a closed-form expression for $\nabla \log p_t(x)$ for nonlinear SDEs with state-independent diffusion coefficients. These advancements provide fresh theoretical insights into the smoothness and structure of probability densities and practical implications for score-based generative modelling, including the design and analysis of new diffusion models. Moreover, our findings promote the adoption of the robust Malliavin calculus framework in machine learning research. These results directly apply to various pure and applied mathematics fields, such as generative modelling, the study of SDEs driven by fractional Brownian motion, and the Fokker--Planck equations associated with nonlinear SDEs.

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