Sample and Map from a Single Convex Potential: Generation using Conjugate Moment Measures

• URL: http://arxiv.org/abs/2503.10576v1
• Date: Thu, 13 Mar 2025 17:28:44 GMT
• Title: Sample and Map from a Single Convex Potential: Generation using Conjugate Moment Measures
• Authors: Nina Vesseron, Louis Béthune, Marco Cuturi,
• Abstract summary: A common approach to generative modeling is to split model-fitting into two blocks: define first how to sample noise (e.g. Gaussian) and choose next what to do with it.<n>We explore in this alternative route that sampling and mapping.<n>We find inspiration in moment measures, a result that states for any measure $mathbbRd$ there exists a unique potential $u$rho that $rho=nabla u,sharp,e-u$.
• Score: 22.7776491836979
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A common approach to generative modeling is to split model-fitting into two blocks: define first how to sample noise (e.g. Gaussian) and choose next what to do with it (e.g. using a single map or flows). We explore in this work an alternative route that ties sampling and mapping. We find inspiration in moment measures, a result that states that for any measure $\rho$ supported on a compact convex set of $\mathbb{R}^d$, there exists a unique convex potential $u$ such that $\rho=\nabla u\,\sharp\,e^{-u}$. While this does seem to tie effectively sampling (from log-concave distribution $e^{-u}$) and action (pushing particles through $\nabla u$), we observe on simple examples (e.g., Gaussians or 1D distributions) that this choice is ill-suited for practical tasks. We study an alternative factorization, where $\rho$ is factorized as $\nabla w^*\,\sharp\,e^{-w}$, where $w^*$ is the convex conjugate of $w$. We call this approach conjugate moment measures, and show far more intuitive results on these examples. Because $\nabla w^*$ is the Monge map between the log-concave distribution $e^{-w}$ and $\rho$, we rely on optimal transport solvers to propose an algorithm to recover $w$ from samples of $\rho$, and parameterize $w$ as an input-convex neural network.

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