Abstract— We study the problem of maximizing the aggregated revenue in sensor networks with deadline constraints. Our model is that of a sensor network that is arranged in the fo...
Submodular maximization generalizes many important problems including Max Cut in directed/undirected graphs and hypergraphs, certain constraint satisfaction problems and maximum f...
The problem of deriving joint policies for a group of agents that maximize some joint reward function can be modeled as a decentralized partially observable Markov decision proces...
Ranjit Nair, Milind Tambe, Makoto Yokoo, David V. ...
In contrast to the common belief that OpenMP requires data-parallel extensions to scale well on architectures with non-uniform memory access latency, recent work has shown that it ...
Recently, significant progress has been made on learning structured predictors via coordinated training algorithms such as conditional random fields and maximum margin Markov ne...