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323
Parallel Double Greedy Submodular Maximization
"... Many machine learning problems can be reduced to the maximization of submodular functions. Although well understood in the serial setting, the parallel maximization of submodular functions remains an open area of research with recent results [1] only addressing monotone functions. The optimal algor ..."
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Cited by 1 (0 self)
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algorithm for maximizing the more general class of nonmonotone submodular functions was introduced by Buchbinder et al. [2] and follows a strongly serial doublegreedy logic and program analysis. In this work, we propose two methods to parallelize the doublegreedy algorithm. The first, coordination
Simultaneous Multithreading: Maximizing OnChip Parallelism
, 1995
"... This paper examines simultaneous multithreading, a technique permitting several independent threads to issue instructions to a superscalar’s multiple functional units in a single cycle. We present several models of simultaneous multithreading and compare them with alternative organizations: a wide s ..."
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Cited by 823 (48 self)
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multithreading has the potential to achieve 4 times the throughput of a superscalar, and double that of finegrain multithreading. We evaluate several cache configurations made possible by this type of organization and evaluate tradeoffs between them. We also show that simultaneous multithreading
Maximizing the Spread of Influence Through a Social Network
 In KDD
, 2003
"... Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in gametheoretic settings, and the effects of ..."
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Cited by 990 (7 self)
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the first provable approximation guarantees for efficient algorithms. Using an analysis framework based on submodular functions, we show that a natural greedy strategy obtains a solution that is provably within 63 % of optimal for several classes of models; our framework suggests a general approach
A Unified Continuous Greedy Algorithm for Submodular Maximization
, 2011
"... The study of combinatorial problems with a submodular objective function has attracted much attention in recent years, and is partly motivated by the importance of such problems to economics, algorithmic game theory and combinatorial optimization. Classical works on these problems are mostly combin ..."
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Cited by 22 (5 self)
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immediately implies improved approximations for numerous applications. A simple and elegant method, called “continuous greedy”, successfully tackles this issue for monotone submodular objective functions, however,
Online submodular welfare maximization: Greedy is optimal
"... We prove that no online algorithm (even randomized, against an oblivious adversary) is better than 1/2competitive for welfare maximization with coverage valuations, unless NP = RP. Since the Greedy algorithm is known to be 1/2competitive for monotone submodular valuations, of which coverage is a s ..."
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Cited by 3 (0 self)
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We prove that no online algorithm (even randomized, against an oblivious adversary) is better than 1/2competitive for welfare maximization with coverage valuations, unless NP = RP. Since the Greedy algorithm is known to be 1/2competitive for monotone submodular valuations, of which coverage is a
The power of randomization: Distributed submodular maximization on massive datasets
 In arXiv
, 2015
"... A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization problems. Unfortunately, the resulting submodular optimization problems are often too large to be solved on a single machi ..."
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Cited by 1 (0 self)
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A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization problems. Unfortunately, the resulting submodular optimization problems are often too large to be solved on a single
Bounds on DoubleSided Myopic Algorithms for Unconstrained Nonmonotone Submodular Maximization
, 2014
"... Unconstrained submodular maximization captures many NPhard combinatorial optimization problems, including MaxCut, MaxDiCut, and variants of facility location problems. Recently, Buchbinder et al. [8] presented a surprisingly simple linear time randomized greedylike online algorithm that achiev ..."
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Unconstrained submodular maximization captures many NPhard combinatorial optimization problems, including MaxCut, MaxDiCut, and variants of facility location problems. Recently, Buchbinder et al. [8] presented a surprisingly simple linear time randomized greedylike online algorithm
Submodular meets Spectral: Greedy Algorithms for Sparse Approximation and
 Dictonary Selection, 2011. http://arxiv.org/abs/1102.3975. Diekhoff, G. Statistics for the Social and Behavioral Sciences
"... We study the problem of selecting a subset of k random variables from a large set, in order to obtain the best linear prediction of another variable of interest. This problem can be viewed in the context of both feature selection and sparse approximation. We analyze the performance of widely used gr ..."
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Cited by 29 (1 self)
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greedy heuristics, using insights from the maximization of submodular functions and spectral analysis. We introduce the submodularity ratio as a key quantity to help understand why greedy algorithms perform well even when the variables are highly correlated. Using our techniques, we obtain the strongest
Optimal Approximation for the Submodular Welfare Problem in the value oracle model
 STOC'08
, 2008
"... In the Submodular Welfare Problem, m items are to be distributed among n players with utility functions wi: 2 [m] → R+. The utility functions are assumed to be monotone and submodular. Assuming that player i receives a set of items Si, we wish to maximize the total utility Pn i=1 wi(Si). In this pap ..."
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Cited by 123 (13 self)
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)approximation is achieved by a uniformly random solution. Using the pipage rounding technique [1, 2], we obtain a (1 − 1/e)approximation for submodular maximization subject to any matroid constraint. The continuous greedy algorithm has a potential of wider applicability, which we demonstrate on the examples
Maximizing a Submodular Set Function subject to a Matroid Constraint (Extended Abstract)
 PROC. OF 12 TH IPCO
, 2007
"... Let f: 2 N → R + be a nondecreasing submodular set function, and let (N, I) be a matroid. We consider the problem maxS∈I f(S). It is known that the greedy algorithm yields a 1/2approximation [9] for this problem. It is also known, via a reduction from the maxkcover problem, that there is no (1 ..."
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Cited by 112 (14 self)
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Let f: 2 N → R + be a nondecreasing submodular set function, and let (N, I) be a matroid. We consider the problem maxS∈I f(S). It is known that the greedy algorithm yields a 1/2approximation [9] for this problem. It is also known, via a reduction from the maxkcover problem
Results 1  10
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323