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Found 3 papers, 1 papers with code
Submodular Maximization Beyond Non-negativity: Guarantees, Fast Algorithms, and Applications
It is generally believed that submodular functions -- and the more general class of $\gamma$-weakly submodular functions -- may only be optimized under the non-negativity assumption $f(S) \geq 0$.
A New Framework for Distributed Submodular Maximization
A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization problems.
The Power of Randomization: Distributed Submodular Maximization on Massive Datasets
A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization problems.
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