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Justin Ward

2 accepted papers

2019

Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications

ICML 2019oral

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 $f(S) \geq 0$. In this paper, we show that once the function is expressed as the difference $f = g - c$, where $g$ is monoto…

2015

The Power of Randomization: Distributed Submodular Maximization on Massive Datasets

ICML 2015poster

A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization problems. Unfortunately, the resulting submodular optimization problems are often too large to be solved on a single machine…

Cited by 114SourcePDFScholar