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The standard greedy algorithm has been recently shown to enjoy approximation guarantees for constrained non-submodular nondecreasing set function maximization.
An analysis of approximations for maximizing submodular set functions—ii
Marshall L Fisher, George L Nemhauser, and Laurence A Wolsey · 1978
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An analysis of approximations for maximizing submodular set functions—i
George L Nemhauser, Laurence A Wolsey, and Marshall L Fisher · 1978
Earlier work this paper cites.
Submodular set functions, matroids and the greedy algorithm: tight worst-case bounds and some generalizations of the rado-edmonds theorem
Michele Conforti and Gérard Cornuéjols · 1984
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Using bayesian networks to analyze expression data
Nir Friedman, Michal Linial, Iftach Nachman, and Dana Pe’er · 2000
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Combinatorial optimization: polyhedra and efficiency , volume 24
Alexander Schrijver · 2003
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Inferring cellular networks using probabilistic graphical models
Nir Friedman · 2004
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Submodular functions and optimization , volume 58
Satoru Fujishige · 2005
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Near-optimal nonmyopic value of information in graphical models
Andreas Krause and Carlos E Guestrin · 2005
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Stable signal recovery from incomplete and inaccurate measurements
Emmanuel J Candes, Justin K Romberg, and Terence Tao · 2006
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Uriel Feige and Jan Vondrak · 2006
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Combinatorial auctions with decreasing marginal utilities
Benny Lehmann, Daniel Lehmann, and Noam Nisan · 2006
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Analysis of greedy approximations with nonsubmodular potential functions
Ding-Zhu Du, Ronald L Graham, Panos M Pardalos, Peng-Jun Wan, Weili Wu, and Wenbo Zhao · 2008
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Near-optimal sensor placements in gaussian processes: Theory, efficient algorithms and empirical studies
Andreas Krause, Ajit Singh, and Carlos Guestrin · 2008
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Tight information-theoretic lower bounds for welfare maximization in combinatorial auctions
Vahab Mirrokni, Michael Schapira, and Jan Vondrák · 2008
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Optimal approximation for the submodular welfare problem in the value oracle model
Jan Vondrák · 2008
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On maximizing welfare when utility functions are subadditive
Uriel Feige · 2009
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Measuring user influence in twitter: The million follower fallacy
M. Cha, H. Haddadi, F. Benevenuto, and K. Gummadi · 2010
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Inferring regulatory networks from expression data using tree-based methods
Alexandre Irrthum, Louis Wehenkel, Pierre Geurts, et al · 2010
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Learning gaussian tree models: Analysis of error exponents and extremal structures
Vincent YF Tan, Animashree Anandkumar, and Alan S Willsky · 2010
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Jan Vondrák · 2010
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L. Backstrom, E. Bakshy, J. M. Kleinberg, T. M. Lento, and I. Rosenn · 2011
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Maximizing a monotone submodular function subject to a matroid constraint
Gruia Calinescu, Chandra Chekuri, Martin Pál, and Jan Vondrák · 2011
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Submodular meets spectral: Greedy algorithms for subset selection, sparse approximation and dictionary selection
Abhimanyu Das and David Kempe · 2011
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Greedy column subset selection: New bounds and distributed algorithms
Jason Altschuler, Aditya Bhaskara, Gang Fu, Vahab Mirrokni, Afshin Rostamizadeh, and Morteza Zadimoghaddam · 2016
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Cvxpy: A python-embedded modeling language for convex optimization
Steven Diamond and Stephen Boyd · 2016
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Restricted strong convexity implies weak submodularity
Ethan R Elenberg, Rajiv Khanna, Alexandros G Dimakis, and Sahand Negahban · 2016
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Maximization of approximately submodular functions
Thibaut Horel and Yaron Singer · 2016
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Smart broadcasting: Do you want to be seen?
M. Karimi, E. Tavakoli, M. Farajtabar, L. Song, and M. Gomez-Rodriguez · 2016
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Guarantees for greedy maximization of non-submodular functions with applications
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Allan Borodin, Dai Tri Man Le, and Yuli Ye · 2014
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Andrew An Bian, Joachim M Buhmann, Andreas Krause, and Sebastian Tschiatschek · 2017
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Gradient methods for submodular maximization
Hamed Hassani, Mahdi Soltanolkotabi, and Amin Karbasi · 2017
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Asia J Biega, Krishna P Gummadi, and Gerhard Weikum · 2018
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