Results 11 to 20 of about 12,182 (176)
Weakly Submodular Functions [PDF]
, 2014 Submodular functions are well-studied in combinatorial optimization, game theory and economics. The natural diminishing returns property makes them suitable for many applications. We study an extension of monotone submodular functions, which we call {\em
Borodin, Allan +2 more
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Resilient Monotone Submodular Function Maximization [PDF]
2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017 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.
Gatsis, Konstantinos +3 more
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Maximizing Symmetric Submodular Functions [PDF]
ACM Transactions on Algorithms, 2016 Symmetric submodular functions are an important family of submodular functions capturing many interesting cases including cut functions of graphs and hypergraphs.
Feldman, Moran
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Some Results about the Contractions and the Pendant Pairs of a Submodular System [PDF]
Sahand Communications in Mathematical Analysis, 2019 Submodularity is an important property of set functions with deep theoretical results and various applications. Submodular systems appear in many applicable area, for example machine learning, economics, computer vision, social science, game theory ...
Saeid Hanifehnezhad, Ardeshir Dolati
doaj +1 more source
Learning submodular functions [PDF]
Proceedings of the forty-third annual ACM symposium on Theory of computing, 2011 There has been much interest in the machine learning and algorithmic game theory communities on understanding and using submodular functions. Despite this substantial interest, little is known about their learnability from data. Motivated by applications, such as pricing goods in economics, this paper considers PAC-style learning of submodular ...
Maria-Florina Balcan +1 more
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Submodular function minimization and polarity [PDF]
Mathematical Programming, 2021 Using polarity, we give an outer polyhedral approximation for the epigraph of set functions. For a submodular function, we prove that the corresponding polar relaxation is exact; hence, it is equivalent to the Lov sz extension. The polar approach provides an alternative proof for the convex hull description of the epigraph of a submodular function ...
Alper Atamtürk, Vishnu Narayanan
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Subquadratic submodular function minimization [PDF]
Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, 2017 Submodular function minimization (SFM) is a fundamental discrete optimization problem which generalizes many well known problems, has applications in various fields, and can be solved in polynomial time. Owing to applications in computer vision and machine learning, fast SFM algorithms are highly desirable. The current fastest algorithms [Lee, Sidford,
Chakrabarty, Deeparnab +3 more
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Submodular Functions are Noise Stable [PDF]
Proceedings of the Twenty-Third Annual ACM-SIAM Symposium on Discrete Algorithms, 2012 We show that all non-negative submodular functions have high {\em noise-stability}. As a consequence, we obtain a polynomial-time learning algorithm for this class with respect to any product distribution on $\{-1,1\}^n$ (for any constant accuracy parameter $ $). Our algorithm also succeeds in the agnostic setting. Previous work on learning submodular
Cheraghchi, M +3 more
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Mathematics, 2021 In this paper, we consider parallel-machine scheduling with release times and submodular penalties (P|rj,reject|Cmax+π(R)), in which each job can be accepted and processed on one of m identical parallel machines or rejected, but a penalty must paid if a ...
Wencheng Wang, Xiaofei Liu
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Journal of Mathematics, 2023 In this paper, we focus on solving the vector scheduling problem with submodular penalties on parallel machines. We are given n jobs and m parallel machines, where each job is associated with a d-dimensional vector.
Bihui Cheng, Wencheng Wang
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