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A general design-assisted regression framework in which the estimating criterion depends on both the conditional model for $Y \mid \bfX$ and structured features of the covariate distribution, which improves estimation while preserving first-order prediction performance.
RiVaT-Fuse is proposed, a reliability-calibrated variational tensor fusion framework that defines fusion as sample-wise latent-state estimation and achieves the strongest overall predictive rank among direct representation-level baselines while improving probability and label stability under perturbation.
DR-LabStack is designed and implemented, a React-Flask web system integrating four externally developed pretrained models: RuleFit, Pruned RuleFit, Elaborative XGBoost, and Two-level Ensemble, a reusable interaction and serving workflow for heterogeneous DR models.
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