Go to ICML 2025 Conference homepageThe Limits of Tractable Marginalization
TL;DR: We show that the many known models for tractable marginalization have not yet captured all functions with tractable marginalization.
Abstract: Marginalization -- summing a function over all assignments to a subset of its inputs -- is a fundamental computational problem with applications from probabilistic inference to formal verification.
Despite its computational hardness in general, there exist many classes of functions (e.g., probabilistic models) for which marginalization remains tractable, and they can all be commonly expressed by arithmetic circuits computing multilinear polynomials.
This raises the question, can *all* functions with polynomial time marginalization algorithms be succinctly expressed by such circuits? We give a negative answer, exhibiting simple functions with tractable marginalization yet no efficient representation by known models, assuming $\\mathsf{FP} \\neq \\#\\mathsf{P}$ (an assumption implied by $\\mathsf{P} \\neq \\mathsf{NP}$).
To this end, we identify a hierarchy of complexity classes corresponding to stronger forms of marginalization, all of which are efficiently computable on the known circuit models.
We conclude with a completeness result, showing that whenever there is an efficient real RAM performing virtual evidence marginalization for a function, then there are small arithmetic circuits for that function's multilinear representation.
Lay Summary: In this work, we analyse a commonly used architecture (arithmetic circuits computing multilinear polynomials) in probabilistic inference particularly used for marginalization tasks. We prove the theoretical limits on the expressiveness of this architecture, by showing that there are functions for which marginalization is efficient but which can't be succinctly represented by these circuits.
Primary Area: Theory->Probabilistic Methods
Keywords: probabilistic inference, tractable models, expressive-efficiency
Submission Number: 14577
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