Mechanism confirmed, baseline not beaten
2026
CVaR-tail active residual correction
Train a cheap neural surrogate globally, then use an ensemble or bootstrap covariance to identify inputs near the estimated upper-tail boundary and inputs where high-fidelity correction is uncertain. Fit a Tikhonov-regularized residual model on the acquired expensive labels and use the corrected predictor for CVaR estimation or risk-constrained optimization. The acquisition policy deliberately ignores easy central-region samples unless they influence the tail threshold.
Paper: Risk-averse design optimization with CVaR constraints via multifidelity tail-region correction
arXiv:2608.29222