Random sampling is an appealing approach to build synopses of large data streams because random samples can be used for a broad spectrum of analytical tasks. Users are often inter...
A silent self-stabilizing asynchronous distributed algorithms is given for constructing a kdominating set, and hence a k-clustering, of a connected network of processes with uniqu...
Ajoy Kumar Datta, Lawrence L. Larmore, Priyanka Ve...
Abstract. We consider the problem of maintaining polynomial and exponential decay aggregates of a data stream, where the weight of values seen from the stream diminishes as time el...
Abstract--We give sublinear-time approximation algorithms for some optimization problems arising in machine learning, such as training linear classifiers and finding minimum enclos...
Kenneth L. Clarkson, Elad Hazan, David P. Woodruff
We present a new polynomial-space algorithm for solving Distributed Constraint Optimization problems (DCOP). The algorithm, called NCBB, is branch and bound search with modificati...