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arXiv Machine Learning By Weizhu Qian

Learning Population Dynamics with Variational Doob-Wasserstein Flow

Read the original on arXiv Machine Learning →

The paper presents Variational Doob-Wasserstein Flow (VDWF), a simulation-free method for learning unbalanced population dynamics on the Riemannian Wasserstein-Fisher-Rao manifold. VDWF first learns a geometry model with a data-dependent metric to capture intrinsic dynamics, then uses Doob's h-transform to incorporate boundary information for trajectory inference. It employs a growth model to learn dynamics and construct intermediate mass samples in WFR space, analyzes convergence via a uniform Log-Sobolev inequality, and demonstrates competitive performance on real-world datasets for both balanced and unbalanced dynamics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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