Learning to Bound: Monotonic, Calibrated Neural Upper Bounds for MPE Inference
This work introduces Learning to Bound (L2B), a data-driven framework that amortizes bound computation cost by training a neural predictor and targets two key properties required for effective search bounds: admissibility and progressive tightening as evidence accumulates.
Akshay Vyas, Hai-Liang Dong, Shivvrat Arya et al.
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