Abstract--In this paper we investigate the sparsity and recognition capabilities of two approximate Bayesian classification algorithms, the multi-class multi-kernel Relevance Vecto...
Ioannis Psorakis, Theodoros Damoulas, Mark A. Giro...
Many signals of interest are corrupted by faults of an unknown type. We propose an approach that uses Gaussian processes and a general “fault bucket” to capture a priori uncha...
Michael A. Osborne, Roman Garnett, Kevin Swersky, ...
This paper continues the investigation of the connection between probabilistically checkable proofs (PCPs) and the approximability of NP-optimization problems. The emphasis is on p...
We describe a new LLL-type algorithm, H-LLL, that relies on Householder transformations to approximate the underlying Gram-Schmidt orthogonalizations. The latter computations are ...
Abstract—We study a network model in which each network link is associated with a set of delays and costs. These costs are a function of the delays and reflect the prices paid i...