Abstract. Aggregation is an important building block of modern distributed applications, allowing the determination of meaningful properties (e.g. network size, total storage capac...
Abstracts "Mixtures at the Interface" David Scott, Rice University Mixture modeling provides an effective framework for complex, high-dimensional data. The potential of m...
Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of...
Michael Collins, Amir Globerson, Terry Koo, Xavier...
Abstract. Maximum likelihood (ML) is an increasingly popular optimality criterion for selecting evolutionary trees [Felsenstein 1981]. Finding optimal ML trees appears to be a very...
We define reactive simulatability for general asynchronous systems. Roughly, simulatability means that a real system implements an ideal system (specification) in a way that pre...