Détail du document
Identifiant
oai:arXiv.org:2402.16442
Sujet
Computer Science - Machine Learnin... Computer Science - Artificial Inte... Computer Science - Computer Vision... Computer Science - Distributed, Pa... Mathematics - Optimization and Con...Auteur
Böther, Maximilian Sebastian, Abraham Awasthi, Pranjal Klimovic, Ana Ramalingam, SrikumarCatégorie
Computer Science
Année
2024
Date de référencement
28/02/2024
Mots clés
selection algorithm distributed science computerRésumé
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 fit the target subset.
In this paper, we relax the requirement of having a central machine for the target subset by proposing a novel distributed bounding algorithm with provable approximation guarantees.
The algorithm iteratively bounds the minimum and maximum utility values to select high quality points and discard the unimportant ones.
When bounding does not find the complete subset, we use a multi-round, partition-based distributed greedy algorithm to identify the remaining subset.
We show that these algorithms find high quality subsets on CIFAR-100 and ImageNet with marginal or no loss in quality compared to centralized methods, and scale to a dataset with 13 billion points.
Böther, Maximilian,Sebastian, Abraham,Awasthi, Pranjal,Klimovic, Ana,Ramalingam, Srikumar, 2024, On Distributed Larger-Than-Memory Subset Selection With Pairwise Submodular Functions
