We propose the framework of mutual information kernels for learning covariance kernels, as used in Support Vector machines and Gaussian process classifiers, from unlabeled task da...
We present a novel method for approximate inference in Bayesian models and regularized risk functionals. It is based on the propagation of mean and variance derived from the Lapla...
Alexander J. Smola, Vishy Vishwanathan, Eleazar Es...
The Bayesian paradigm apparently only sometimes gives rise to Occam's Razor; at other times very large models perform well. We give simple examples of both kinds of behaviour...
This paper addresses cooperative Time Division Duplex (TDD) relaying in the multiple-antenna case with full Channel State Information (CSI), i.e. assuming perfect knowledge of all ...
In this article we review some recent interactions between harmonic analysis and data compression. The story goes back of course to Shannon’s R(D) theory in the case of Gaussian...
David L. Donoho, Martin Vetterli, Ronald A. DeVore...