Zero Flux: Flow-Based Comparison of High-Dimensional Discrete Distributions

Abstract:Comparing two high-dimensional discrete distributions has always been a challenging task due to the exponentially growing state space and complex changes in interactions. A recent work suggests comparing distributions through a vector field trained using flow matching between two continuous distributions. The resulting vector field at mid-point vanishes if and only if two distributions identical. However, such a flow-based criterion does not naturally apply to discrete distributions. We extend this principle to the discrete domain and introduce the \emph{Zero Flux} criterion, a discrepancy based on local probability fluxes. Under independent coupling, we show that all local probability fluxes vanish at the midpoint if and only if two distributions are the same. This discrepancy decomposes the joint distributional difference into smaller, local contributions and can be efficiently estimated from samples. We establish finite sample error bounds for our estimator. Experiments on synthetic and real categorical data demonstrate reliable recovery of sparse dependence signals and stable tracking of distribution shifts in high dimensions.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2610.01472 [stat.ML]

(or arXiv:2610.01472v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2610.01472 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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