Given that only the ranking induced by the ground-truth distances in d is important, as opposed to the absolute values themselves, using an MSE loss over-penalizes the model by ignoring ranking equivalence between different predictions.
we propose to minimize a pair-wise learning-to-rank loss, similar to those proposed in (Burges et al., 2005).