2017
Scaling Submodular Maximization via Pruned Submodularity Graphs
AISTATS 2017poster
We propose a new random pruning method (called “submodular sparsification (SS)”) to reduce the cost of submodular maximization. The pruning is applied via a “submodularity graph” over the $n$ ground elements, where each directed edge is associated with a pairwise dependency defined by the submodular…