Efficient Submodular Optimization under Noise: Local Search is RobustDownload PDF

Published: 31 Oct 2022, Last Modified: 11 Jan 2023NeurIPS 2022 AcceptReaders: Everyone
Keywords: submodular, optimization, noise, local search
Abstract: The problem of monotone submodular maximization has been studied extensively due to its wide range of applications. However, there are cases where one can only access the objective function in a distorted or noisy form because of the uncertain nature or the errors involved in the evaluation. This paper considers the problem of constrained monotone submodular maximization with noisy oracles introduced by Hassidim and Singer (2017). For a cardinality constraint, we propose an algorithm achieving a near-optimal (1-1/e-O(epsilon))-approximation guarantee (for arbitrary epsilon > 0) with only a polynomial number of queries to the noisy value oracle, which improves the exponential query complexity of Singer and Hassidim (2018). For general matroid constraints, we show the first constant approximation algorithm in the presence of noise. Our main approaches are to design a novel local search framework that can handle the effect of noise and to construct certain smoothing surrogate functions for noise reduction.
TL;DR: This paper designs a novel local search framework that can handle the effect of noise and achieve near-optimal approximation guarantees for submodular maximization with polynomial queries.
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