SLP-ProbHard: Probabilistic Hard-Constrained Learning via Structural Latent Parameterization
Many probabilistic predictors must satisfy exact structure in every stochastic realization, yet common hard-constraint approaches form predictions in ambient coordinates and then correct or project them. We introduce SLP-ProbHard, a cross-family, representation-centered framework for probabilistic hard-constrained learning when explicit structural parameterizations are available. Its core object, a Structural Feasible Latent Parameterization (SFLP), combines a structural latent law $Z \sim P^Z_θ(\cdot\mid x)$ with a feasible map $Y=h_φ(x,Z)$ that satisfies the constraint for every latent realization. Together these components define the predictive law itself, including its support and bou...
| Authors | Wondesen Teshome Bekele, Marco D'Oria |
|---|---|
| Primary category | cs.LG |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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