2020
DOI: 10.48550/arxiv.2005.02578
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Differentiable Greedy Submodular Maximization: Guarantees, Gradient Estimators, and Applications

Shinsaku Sakaue

Abstract: Motivated by, e.g., sensitivity analysis and end-to-end learning, the demand for differentiable optimization algorithms has been significantly increasing. In this paper, we establish a theoretically guaranteed versatile framework that makes the greedy algorithm for monotone submodular function maximization differentiable. We smooth the greedy algorithm via randomization, and prove that it almost recovers original approximation guarantees in expectation for the cases of cardinality and κ-extensible system const… Show more

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