Reviews: Uncoupled Regression from Pairwise Comparison Data

This paper proposes two novel approaches for the uncoupled regression problem, in which the correspondance between the input data and the targets is not known. Instead these methods use unlabeled data and pairwise comparisons of the targets. The first approach is the risk approximation approach (RA) and it minimizes an approximation of the risk defined based on the expected Bregman divergence. The second approach, called the target transformation (TT) approach, consists in mapping the target variable to a uniformly distributed random variable using the cumulative distribution function. Estimation error bounds are derived for each method.