A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization problems. A lot of recent effort has been devoted to developing distributed algorithms for these problems. However, these results suffer from high number of rounds, suboptimal approximation ratios, or both. We develop a framework for bringing existing algorithms in the sequential setting to the distributed setting, achieving near optimal approximation ratios for many settings in only a constant number of MapReduce rounds. Our techniques also give a fast sequential algorithm for non-monotone maximization subject to a matroid constraint
Thesis (Ph.D.)--University of Washington, 2015In this dissertation, we explore a class of unifying a...
We present an optimal, combinatorial 1−1/e approximation algorithm for monotone submodular optimizat...
In this work, we give a new parallel algorithm for the problem of maximizing a non-monotone dimini...
A wide variety of problems in machine learning, including exemplar clustering, document summarizatio...
A wide variety of problems in machine learning, including exemplar clustering, document summarizatio...
MapReduce (MR) algorithms for maximizing monotone, submodular functions subject to a cardinality con...
A wide variety of problems in machine learning, including exemplar clustering, document sum-marizati...
Distributed maximization of a submodular function in the MapReduce model has received much attention...
Many large-scale machine learning problems (such as clustering, non-parametric learning, kernel mach...
We study combinatorial, parallelizable algorithms for maximization of a submodular function, not nec...
We present an optimal, combinatorial 1-1/e approximation algorithm for monotone submodular optimizat...
Submodular function maximization is a central problem in combinatorial optimization, generalizing ma...
Abstract—Maximization of submodular set functions arises in wireless applications such as scheduling...
Many machine learning problems can be reduced to the maximization of sub-modular functions. Although...
A litany of questions from a wide variety of scientific disciplines can be cast as non-monotone subm...
Thesis (Ph.D.)--University of Washington, 2015In this dissertation, we explore a class of unifying a...
We present an optimal, combinatorial 1−1/e approximation algorithm for monotone submodular optimizat...
In this work, we give a new parallel algorithm for the problem of maximizing a non-monotone dimini...
A wide variety of problems in machine learning, including exemplar clustering, document summarizatio...
A wide variety of problems in machine learning, including exemplar clustering, document summarizatio...
MapReduce (MR) algorithms for maximizing monotone, submodular functions subject to a cardinality con...
A wide variety of problems in machine learning, including exemplar clustering, document sum-marizati...
Distributed maximization of a submodular function in the MapReduce model has received much attention...
Many large-scale machine learning problems (such as clustering, non-parametric learning, kernel mach...
We study combinatorial, parallelizable algorithms for maximization of a submodular function, not nec...
We present an optimal, combinatorial 1-1/e approximation algorithm for monotone submodular optimizat...
Submodular function maximization is a central problem in combinatorial optimization, generalizing ma...
Abstract—Maximization of submodular set functions arises in wireless applications such as scheduling...
Many machine learning problems can be reduced to the maximization of sub-modular functions. Although...
A litany of questions from a wide variety of scientific disciplines can be cast as non-monotone subm...
Thesis (Ph.D.)--University of Washington, 2015In this dissertation, we explore a class of unifying a...
We present an optimal, combinatorial 1−1/e approximation algorithm for monotone submodular optimizat...
In this work, we give a new parallel algorithm for the problem of maximizing a non-monotone dimini...