2025
Learning to Condition: A Neural Heuristic for Scalable MPE Inference
NeurIPS 2025poster
We introduce learning to condition (L2C), a scalable, data-driven framework for accelerating Most Probable Explanation (MPE) inference in Probabilistic Graphical Models (PGMs)—a fundamentally intractable problem. L2C trains a neural network to score variable-value assignments based on their utility…