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cs.LG2026
Learning to Bound: Monotonic, Calibrated Neural Upper Bounds for MPE Inference
Akshay Vyas, Hailiang Dong, Shivvrat Arya +4
Probabilistic graphical models (PGMs) compactly encode structured joint distributions, but Most Probable Explanation (MPE), which finds the highest-probability assignment to unobse…
cs.AI2026
Learning to Guide Local Search for MPE Inference in Probabilistic Graphical Models
Brij Malhotra, Shivvrat Arya, Tahrima Rahman +1
Most Probable Explanation (MPE) inference in Probabilistic Graphical Models (PGMs) is a fundamental yet computationally challenging problem arising in domains such as diagnosis, pl…