A semi-supervised teacher-student framework enables neural networks to proxy CVaR portfolio optimization using synthetic data augmentation for scarce labels and regime shifts.
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Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training
A semi-supervised teacher-student framework enables neural networks to proxy CVaR portfolio optimization using synthetic data augmentation for scarce labels and regime shifts.