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Locality-sensitive hashing scheme based on p-stable distributions
TLDR
We present a novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem under lp norm, based on p-stable distributions. Expand
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Maximizing Non-monotone Submodular Functions
TLDR
In this paper, we design the first constant-factor approximation algorithms for maximizing nonnegative (non-monotone) submodular functions. Expand
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Optimal marketing strategies over social networks
We discuss the use of social networks in implementing viral marketing strategies. While influence maximization has been studied in this context (see Chapter 24 of [10]), we study revenueExpand
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Maximizing Non-Monotone Submodular Functions
TLDR
Submodular maximization generalizes many important problems including Max Cut in directed/undirected graphs, certain constraint satisfaction problems and maximum facility location problems. Expand
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Online Stochastic Matching: Beating 1-1/e
TLDR
Our main result is a $0.67$-approximation online algorithm for stochastic bipartite matching, breaking this $1 - {1\over e}$ barrier. Expand
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Approximating submodular functions everywhere
TLDR
We consider the problem of approximating a non-negative, monotone, submodular function f on a ground set of size n everywhere, after only poly(n) oracle queries. Expand
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Sink equilibria and convergence
We introduce the concept of a sink equilibrium. A sink equilibrium is a strongly connected component with no outgoing arcs in the strategy profile graph associated with a game. The strategy profileExpand
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Non-monotone submodular maximization under matroid and knapsack constraints
TLDR
Submodular function maximization is a central problem in combinatorial optimization, generalizing many important problems including Max Cut in directed/undirected graphs and in hypergraphs, certain constraint satisfaction problems, maximum entropy sampling, and maximum facility location problems. Expand
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Maximizing Non-Monotone Submodular Functions
TLDR
Submodular maximization generalizes many important problems including Max Cut in directed/undirected graphs and hypergraphs, certain constraint satisfaction problems and maximum facility location problems. Expand
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Tight approximation algorithms for maximum general assignment problems
A separable assignment problem (SAP) is defined by a set of bins and a set of items to pack in each bin; a value, f ij , for assigning item j to bin i; and a separate packing constraint for each binExpand
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