AI RESEARCH
Wasserstein Distributionally Robust Risk-Sensitive Estimation via Conditional Value-at-Risk
arXiv CS.LG
•
ArXi:2604.18546v1 Announce Type: new We propose a distributionally robust approach to risk-sensitive estimation of an unknown signal x from an observed signal y. The unknown signal and observation are modeled as random vectors whose joint probability distribution is unknown, but assumed to belong to a given type-2 Wasserstein ball of distributions, termed the ambiguity set. The performance of an estimator is measured according to the conditional value-at-risk (CVaR) of the squared estimation error.