The use of Mercer kernel methods in statistical learning theory provides for strong learning capabilities, as seen in kernel principal component analysis and support vector machin...
The theory of compressed sensing tells a dramatic story that sparse signals can be reconstructed near-perfectly from a small number of random measurements. However, recent work ha...
We present a framework for defining abstract interpreters for liveness properties, in particular program termination. The framework makes use of the theory of metric spaces to defi...
The paper addresses the problem of learning a regression model parameterized by a fixed-rank positive semidefinite matrix. The focus is on the nonlinear nature of the search space...
In this paper we present a general framework for the comparison of intervals when preference relations have to established. The use of intervals in order to take into account impr...