We address the problem of designing distributed algorithms for large scale networks that are robust to Byzantine faults. We consider a message passing, full information model: the ...
Valerie King, Steven Lonargan, Jared Saia, Amitabh...
We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic pro...
Mark Steyvers, Padhraic Smyth, Michal Rosen-Zvi, T...
We define the concept of dependence among multiple variables using maximum entropy techniques and introduce a graphical notation to denote the dependencies. Direct inference of in...
This paper investigates the potential information provided to the user by the uncertainty measures applied to the possibility distributions associated with the spatial units of an ...
One of the key motivating factors for information providers to use personalisation is to maximise the benefit to the user in accessing their content. However, traditionally such s...