This paper derives a novel algorithm for solving the dual DRLR problem when \kappa \infty (i.e. the labels may change during transport). The algorithm performs a golden section search for \lambda, within which the sub-problem for optimal \beta, fixing \lambda, is solved by an ADMM algorithm. The ADMM algorithm differs from typical ADMM approaches in two ways: (1) the \beta-update is ill-conditioned, requiring a careful choice of iterative method, while (2) the auxiliary \mu update is locally strongly convex, enabling the use of a first-order (not quadratic) approximation with a fixed step size. I see three theoretical contributions: 1. An upper bound on optimal \lambda, stated in Proposition 1, which enables the golden section search.

The paper proposes to solve the Wasserstein distributionally robust logistic regression problem by using an ADMM-style algorithm. All reviewers and myself find it interesting and can have practical and theoretical impacts.