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News/Learning to Bound: Monotonic, Calibrated Neural Upper Bounds for MPE Inference
arxiv
PublishedOctober 31, 2026 at 4:00 AM

Learning to Bound: Monotonic, Calibrated Neural Upper Bounds for MPE Inference

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arXiv:2610.39305v1 Announce Type: cross Abstract: Probabilistic graphical models (PGMs) compactly encode structured joint distributions, but Most Probable Explanation (MPE), which finds the highest-probability assignment to unobserved variables, remains computationally intractable in large, densely

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