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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…
math.OC2026
Optimality of Affine Policies in Distributionally Robust Linear-Quadratic Control with Temporally Correlated Noise
Jakob Nylöf, Daniel Kuhn, John Lygeros +1
We study finite-horizon distributionally robust linear-quadratic control with disturbances that can be arbitrarily correlated in time. The ambiguity set is modeled as a single 2-Wa…
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…