Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training

This paper proposes a machine learning-assisted portfolio optimization framework designed for low data environments and regime uncertainty. It uses a teacher-student pipeline where a Conditional Value at Risk (CVaR) optimizer generates labels, and neural models are trained using both real and synthetically augmented data to overcome observation scarcity.