On Distributed Larger-Than-Memory Subset Selection With Pairwise Submodular Functions

arXiv:2402.16442v1 Announce Type: new
Abstract: Many learning problems hinge on the fundamental problem of subset selection, i.e., identifying a subset of important and representative points. For example, selecting the most significant samples in ML training cannot only reduce training costs but also enhance model quality. Submodularity, a discrete analogue of convexity, is commonly used for solving subset selection problems. However, existing algorithms for optimizing submodular functions are sequential, and the prior distributed methods require at least one central machine to …

abstract arxiv costs cs.ai cs.cv cs.dc cs.lg distributed example functions hinge math.oc memory quality reduce samples training training costs type