Doing well with less! On Sampling Techniques for Empirical Pairwise Loss Estimation/Minimization

ArXi:2606.02345v1 Announce Type: cross Many machine learning problems, including similarity learning, ranking, and clustering, rely on empirical pairwise loss functions whose quadratic computational cost quickly becomes prohibitive at scale. We nstrate how a frugal approach that retains only a fraction of the available information on pairs can achieve estimation or optimization performance comparable to that obtained by using all pairs, by leveraging survey sampling techniques.