Resilient Monotone Submodular Function Maximization
2017 IEEE 56th Annual Conference on Decision and Control (CDC)(2017)
摘要
In this paper, we focus on applications in machine learning, optimization, and control that call for the resilient selection of a few elements, e.g. features, sensors, or leaders, against a number of adversarial denial-of-service attacks or failures. In general, such resilient optimization problems are hard, and cannot be solved exactly in polynomial time, even though they often involve objective functions that are monotone and submodular. Notwithstanding, in this paper we provide the first scalable, curvature-dependent algorithm for their approximate solution, that is valid for any number of attacks or failures, and which, for functions with low curvature, guarantees superior approximation performance. Notably, the curvature has been known to tighten approximations for several non-resilient maximization problems, yet its effect on resilient maximization had hitherto been unknown. We complement our theoretical analyses with supporting empirical evaluations.
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关键词
nonresilient maximization problems,resilient monotone submodular function maximization,machine learning,resilient selection,denial-of-service attacks,general optimization problems,polynomial time,objective functions,scalable algorithm,approximation performance
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