Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach

ArXi:2606.00265v1 Announce Type: cross We study quantile regression in an extrapolation regime where the covariate takes unusually large values. Under regular variation assumptions, extreme observations can be effectively characterized through their angular components, enabling learning strategies that focus on the angle of the most extreme observations. This approach is formalized through the minimization of an asymptotic conditional risk that localizes learning in the tail of the covariate distribution.