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Introduces TopoTuner, a topology-guided fine-tuning framework that selectively freezes attention projection matrices by measuring topological drift via Wasserstein distances between persistence diagrams. It achieves competitive performance to full fine-tuning while training only 1-2% of parameters and outperforms LoRA in most settings.
Proposes learned predictive ambiguity sets (LPAS) for distributionally robust optimization, where a deep contextual model outputs a nominal scenario distribution, state-dependent Wasserstein radius, and ground metric, trained with decision loss and calibration. Applied to portfolio optimization on S&P 500 data, the method achieves higher returns and Sharpe ratio with reduced conservatism compared to fixed-radius baselines.