Multimarginal Generative Modeling with Stochastic Interpolants

Michael Samuel Albergo, Nicholas Matthew Boffi, Michael Lindsey, Eric Vanden-Eijnden

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Keywords: multi-marginal, unsupervised learning, generative modeling, measure transport, optimal transport

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TL;DR: We introduce a method to generalize flow-based and diffusion based generative models to map between K distributions instead of two, revealing multiway-correspondences between densities.

Abstract: Given a set of $K$ probability densities, we consider the multimarginal generative modeling problem of learning a joint distribution that recovers these densities as marginals. The structure of this joint distribution should identify multi-way correspondences among the prescribed marginals. We formalize an approach to this task within a generalization of the stochastic interpolant framework, leading to efficient learning algorithms built upon dynamical transport of measure. Our generative models are defined by velocity and score fields that can be characterized as the minimizers of simple quadratic objectives, and they are defined on a simplex that generalizes the time variable in the usual dynamical transport framework. The resulting transport on the simplex is influenced by all marginals, and we show that multi-way correspondences can be extracted. The identification of such correspondences has applications to style transfer, algorithmic fairness, and data decorruption. In addition, the multimarginal perspective enables an efficient algorithm for optimizing the dynamical transport cost in the ordinary two-marginal setting. We demonstrate these capacities with several numerical examples.

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Primary Area: generative models

Submission Number: 9021