We address well-studied problems concerning the learnability of parities and halfspaces in the presence of classification noise. Learning of parities under the uniform distributi...
Loopy and generalized belief propagation are popular algorithms for approximate inference in Markov random fields and Bayesian networks. Fixed points of these algorithms correspo...
— Sensor networks (SNETs) for monitoring spatial phenomena has emerged as an area of significant practical interest. We focus on the important problem of detection of distribute...
In this paper, we present SURCH, a novel decentralized algorithm for efficient processing of queries generated in sensor networks. Unlike existing techniques, SURCH is fully dist...
—We apply large deviations theory to study asymptotic performance of running consensus distributed detection in sensor networks. Running consensus is a stochastic approximation t...