Unbounded Density Ratio Estimation and Its Application to Covariate Shift Adaptation

ArXi:2603.29725v1 Announce Type: cross This paper focuses on the problem of unbounded density ratio estimation -- an understudied yet critical challenge in statistical learning -- and its application to covariate shift adaptation. Much of the existing literature assumes that the density ratio is either uniformly bounded or unbounded but known exactly. These conditions are often violated in practice, creating a gap between theoretical guarantees and real-world applicability.