Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (th...
We consider in-network processing via local message passing. The considered setting involves a set of sensors each of which can communicate with a subset of other sensors. There is...
The Densest k-subgraph problem (i.e. find a size k subgraph with maximum number of edges), is one of the notorious problems in approximation algorithms. There is a significant g...
We study a novel clustering problem in which the pairwise relations between objects are categorical. This problem can be viewed as clustering the vertices of a graph whose edges a...
Francesco Bonchi, Aristides Gionis, Francesco Gull...
Many self-organizing and self-adaptive systems use the biologically inspired “gradient” primitive, in which each device in a network estimates its distance to the closest devi...
Jonathan Bachrach, Jacob Beal, Joshua Horowitz, Da...