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cs.LG2026
Adversarial Training for Deep Hedging in Nonstationary Markets
Philipp J. Schneider, Lukas Looser, Antoine Garin +2
Deep hedging learns trading policies from historical or simulated market trajectories, yet under nonstationarity these training paths may not represent future market conditions. We…
cs.LG2026
Soft-Radial Projection for Constrained End-to-End Learning
Philipp J. Schneider, Daniel Kuhn
Integrating hard constraints into deep learning is essential for safety-critical systems. Yet existing constructive layers that project predictions onto constraint boundaries face…