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"Bring Your Own Greedy"+Max: Near-Optimal 1/2-Approximations for Submodular Knapsack
[article]
2019
arXiv
pre-print
The problem of selecting a small-size representative summary of a large dataset is a cornerstone of machine learning, optimization and data science. Motivated by applications to recommendation systems and other scenarios with query-limited access to vast amounts of data, we propose a new rigorous algorithmic framework for a standard formulation of this problem as a submodular maximization subject to a linear (knapsack) constraint. Our framework is based on augmenting all partial Greedy
arXiv:1910.05646v1
fatcat:v3df3bdp3bblnbrold5nzx33xy