arXiv stat.ML
· Papers
Risk-Averse Wasserstein Distributionally Robust Online Learning
arXiv:2602.20403v2 Announce Type: replace-cross Abstract: We study distributionally robust online learning, where a risk-averse learner updates decisions sequentially to guard against worst-case distributions drawn from a Wasserstein ambiguity set centered at past observations. While this paradigm is well understood in